{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "   #  2D Kriging in Python for Engineers and Geoscientists \n",
    "\n",
    "## with GSLIB's KB2D Program Converted to Python\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "\n",
    "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n",
    "\n",
    "This is a tutorial for / demonstration of **Kriging in Python with GSLIB's KB2D program translated to Python, wrappers and reimplementations of other GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). \n",
    "\n",
    "This exercise demonstrates the kriging method in Python with wrappers and reimplimentation of GSLIB methods. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize the kriged map\n",
    "\n",
    "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods:\n",
    "\n",
    "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n",
    "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n",
    "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n",
    "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n",
    "5. locpix - my modification of GSLIB to superimpose a location map on a pixel plot reimplemented with GSLIB parameters using Python methods\n",
    "5. affine - affine correction adjust the mean and standard deviation of a feature reimplemented with GSLIB parameters using Python methods\n",
    "\n",
    "I have also started to translate the GSLIB support subfunctions to Python. Stay tuned.\n",
    "\n",
    "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html.  For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.  Overtime, more of the GSLIB programs will be translated to Python and there will be no need to have the executables.  For this workflow you will need sgsim.exe from GSLIB.com for windows and Mac OS executables from https://github.com/GeostatsGuy/GSLIB_MacOS.  \n",
    "\n",
    "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB.  The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/.  \n",
    "\n",
    "This was my first effort to translate the GSLIB Fortran to Python.  It was pretty easy so I'll start translating other critical GSLIB functions.  I've completed NSCORE, DECLUS, GAM, GAMV and now KB2D as of now.\n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Here are the wrappers and reimplementations of GSLIB method along with two utilities to load GSLIB's Geo-EAS from data files into DataFrames and 2D Numpy arrays.  These are used in the testing workflow.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some GeostatsPy Functions - by Michael Pyrcz, maintained at https://git.io/fNgR7.\n",
    "# A set of functions to provide access to GSLIB in Python.\n",
    "# GSLIB executables: nscore.exe, declus.exe, gam.exe, gamv.exe, vmodel.exe, kb2d.exe & sgsim.exe must be in the working directory \n",
    "# note, since I have now rewritten nscore, gam, gamv and kb2d one can just use these in Python\n",
    "# available in the geostatspy package.\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt                          \n",
    "import random as rand\n",
    "image_type = 'tif'; dpi = 600\n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a 1D or 2D numpy ndarray for use with Python methods\n",
    "def GSLIB2ndarray(data_file,kcol,nx,ny): \n",
    "    colArray = []\n",
    "    if ny > 1:\n",
    "        array = np.ndarray(shape=(ny,nx),dtype=float,order='F')\n",
    "    else:\n",
    "        array = np.zeros(nx)    \n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])          # get the number of columns\n",
    "        for icol in range(0, ncol):   # read over the column names\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            if icol == kcol:\n",
    "                col_name = head[0].split()[0]       \n",
    "        if ny > 1:\n",
    "            for iy in range(0,ny):\n",
    "                for ix in range(0,nx):\n",
    "                    head = [next(myfile) for x in range(1)]\n",
    "                    array[ny-1-iy][ix] = head[0].split()[kcol]\n",
    "        else:\n",
    "            for ix in range(0,nx):\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                array[ix] = head[0].split()[kcol]\n",
    "    return array,col_name \n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a pandas DataFrame for use with Python methods\n",
    "def GSLIB2Dataframe(data_file):\n",
    "    colArray = []\n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])\n",
    "        for icol in range(0, ncol):\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            colArray.append(head[0].split()[0])\n",
    "        data = np.loadtxt(myfile, skiprows = 0)\n",
    "        df = pd.DataFrame(data)\n",
    "        df.columns = colArray\n",
    "        return df\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods, displayed and as image file\n",
    "def hist(array,xmin,xmax,log,cumul,bins,weights,xlabel,title,fig_name):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency')  \n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods (version for subplots)\n",
    "def hist_st(array,xmin,xmax,log,cumul,bins,weights,xlabel,title):  \n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency') \n",
    "    return\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods\n",
    "def locmap(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))    \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods (version for subplots)\n",
    "def locmap_st(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    ixy = 0   \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return im           \n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods\n",
    "def pixelplt(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    plt.figure(figsize=(8,6))\n",
    "    im = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels=np.linspace(vmin,vmax,100))\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im,orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods(version for subplots)\n",
    "def pixelplt_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    x = [];y = []; v = [] # use dummy since scatter plot controls legend min and max appropriately and contour does not!\n",
    "    cs = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(x,y,s=None, c=v, marker=None,cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.clim(vmin,vmax)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods\n",
    "def locpix(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)  \n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods(version for subplots)\n",
    "def locpix_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)  \n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)\n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "\n",
    "# affine distribution correction reimplemented in Python with numpy methods \n",
    "def affine(array,tmean,tstdev): \n",
    "    mean = np.average(array)\n",
    "    stdev = np.std(array)  \n",
    "    array = (tstdev/stdev)*(array - mean) + tmean\n",
    "    return(array)   \n",
    "\n",
    "def make_variogram(nug,nst,it1,cc1,azi1,hmaj1,hmin1,it2=1,cc2=0,azi2=0,hmaj2=0,hmin2=0):\n",
    "    if cc2 == 0:\n",
    "        nst = 1\n",
    "    var = dict([('nug', nug), ('nst', nst), ('it1', it1),('cc1', cc1),('azi1', azi1),('hmaj1', hmaj1), ('hmin1', hmin1), \n",
    "      ('it2', it2),('cc2', cc2),('azi2', azi2),('hmaj2', hmaj2), ('hmin2', hmin2)])\n",
    "    if nug + cc1 + cc2 != 1:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: sill does not sum to 1.0, do not use in simulation \\x1b[0m')\n",
    "    if cc1 < 0 or cc2 < 0 or nug < 0 or hmaj1 < 0 or hmaj2 < 0 or hmin1 < 0 or hmin2 < 0:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: contributions and ranges must be all positive \\x1b[0m')\n",
    "    if hmaj1 < hmin1 or hmaj2 < hmin2:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: major range should be greater than minor range \\x1b[0m')\n",
    "    return var    \n",
    "\n",
    "# sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (.exe must be in working directory)\n",
    "def GSLIB_sgsim_2d_uncond(nreal,nx,ny,hsiz,seed,var,output_file):\n",
    "    import os\n",
    "    import numpy as np \n",
    "    \n",
    "    nug = var['nug']\n",
    "    nst = var['nst']; it1 = var['it1']; cc1 = var['cc1']; azi1 = var['azi1']; hmaj1 = var['hmaj1']; hmin1 = var['hmin1'] \n",
    "    it2 = var['it2']; cc2 = var['cc2']; azi2 = var['azi2']; hmaj2 = var['hmaj2']; hmin2 = var['hmin2']     \n",
    "    max_range = max(hmaj1,hmaj2) \n",
    "    hmn = hsiz * 0.5   \n",
    "    hctab = int(max_range/hsiz)*2 + 1\n",
    "\n",
    "    sim_array = np.random.rand(nx,ny)\n",
    "\n",
    "    file = open(\"sgsim.par\", \"w\")\n",
    "    file.write(\"              Parameters for SGSIM                                         \\n\")\n",
    "    file.write(\"              ********************                                         \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETER:                                                        \\n\")\n",
    "    file.write(\"none                          -file with data                              \\n\")\n",
    "    file.write(\"1  2  0  3  5  0              -  columns for X,Y,Z,vr,wt,sec.var.          \\n\")\n",
    "    file.write(\"-1.0e21 1.0e21                -  trimming limits                           \\n\")\n",
    "    file.write(\"0                             -transform the data (0=no, 1=yes)            \\n\")\n",
    "    file.write(\"none.trn                      -  file for output trans table               \\n\")\n",
    "    file.write(\"1                             -  consider ref. dist (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"none.dat                      -  file with ref. dist distribution          \\n\")\n",
    "    file.write(\"1  0                          -  columns for vr and wt                     \\n\")\n",
    "    file.write(\"-4.0    4.0                   -  zmin,zmax(tail extrapolation)             \\n\")\n",
    "    file.write(\"1      -4.0                   -  lower tail option, parameter              \\n\")\n",
    "    file.write(\"1       4.0                   -  upper tail option, parameter              \\n\")\n",
    "    file.write(\"0                             -debugging level: 0,1,2,3                    \\n\")\n",
    "    file.write(\"nonw.dbg                      -file for debugging output                   \\n\")\n",
    "    file.write(str(output_file) + \"           -file for simulation output                  \\n\")\n",
    "    file.write(str(nreal) + \"                 -number of realizations to generate          \\n\")\n",
    "    file.write(str(nx) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(str(ny) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(\"1 0.0 1.0                     - nz zmn zsiz                                \\n\")\n",
    "    file.write(str(seed) + \"                  -random number seed                          \\n\")\n",
    "    file.write(\"0     8                       -min and max original data for sim           \\n\")\n",
    "    file.write(\"12                            -number of simulated nodes to use            \\n\")\n",
    "    file.write(\"0                             -assign data to nodes (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"1     3                       -multiple grid search (0=no, 1=yes),num      \\n\")\n",
    "    file.write(\"0                             -maximum data per octant (0=not used)        \\n\")\n",
    "    file.write(str(max_range) + \" \" + str(max_range) + \" 1.0 -maximum search  (hmax,hmin,vert) \\n\")\n",
    "    file.write(str(azi1) + \"   0.0   0.0       -angles for search ellipsoid                 \\n\")\n",
    "    file.write(str(hctab) + \" \" + str(hctab) + \" 1 -size of covariance lookup table        \\n\")\n",
    "    file.write(\"0     0.60   1.0              -ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC        \\n\")\n",
    "    file.write(\"none.dat                      -  file with LVM, EXDR, or COLC variable     \\n\")\n",
    "    file.write(\"4                             -  column for secondary variable             \\n\")\n",
    "    file.write(str(nst) + \" \" + str(nug) + \"  -nst, nugget effect                          \\n\")\n",
    "    file.write(str(it1) + \" \" + str(cc1) + \" \" +str(azi1) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj1) + \" \" + str(hmin1) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")\n",
    "    file.write(str(it2) + \" \" + str(cc2) + \" \" +str(azi2) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj2) + \" \" + str(hmin2) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")  \n",
    "    file.close()\n",
    "\n",
    "    os.system('\"sgsim.exe sgsim.par\"')       \n",
    "    sim_array = GSLIB2ndarray(output_file,0,nx,ny)         \n",
    "    return(sim_array[0])\n",
    "\n",
    "# extract regular spaced samples from a model   \n",
    "def regular_sample(array,xmin,xmax,ymin,ymax,step,mx,my,name):\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    iiy = 0\n",
    "    for iy in range(0,ny):\n",
    "        if iiy >= my:\n",
    "            iix = 0\n",
    "            for ix in range(0,nx):\n",
    "                if iix >= mx:\n",
    "                    x.append(xx[ix,iy]);y.append(yy[ix,iy]); v.append(array[ix,iy])\n",
    "                    iix = 0; iiy = 0\n",
    "                iix = iix + 1\n",
    "        iiy = iiy + 1\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df)\n",
    "\n",
    "def random_sample(array,xmin,xmax,ymin,ymax,step,nsamp,name):\n",
    "    import random as rand\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax-1, ymin-1, -1*step))\n",
    "    ny = xx.shape[0]\n",
    "    nx = xx.shape[1]\n",
    "    sample_index = rand.sample(range((nx)*(ny)), nsamp)\n",
    "    for isamp in range(0,nsamp):\n",
    "        iy = int(sample_index[isamp]/ny)\n",
    "        ix = sample_index[isamp] - iy*nx\n",
    "        x.append(xx[iy,ix])\n",
    "        y.append(yy[iy,ix])\n",
    "        v.append(array[iy,ix])\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are a variety of GSLIB subfunctions required by the GSLIB functions that I have converted to Python.  I will continue to convert the subfunctions and include them as needed by the GSLIB functions that I convert. All are available with geostatspy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import numpy.linalg as linalg\n",
    "from numba import jit\n",
    "\n",
    "def dlocate(xx,iis,iie,x):\n",
    "    from bisect import bisect\n",
    "    n = len(xx)\n",
    "    if iie <= iis:\n",
    "        iis = 0; ie = n-1\n",
    "    array = xx[iis:iie-1]  # this is accounting for swith to 0,...,n-1 index\n",
    "    j = bisect(array,x)\n",
    "    return j\n",
    "\n",
    "def dsortem(ib,ie,a,iperm,b=0,c=0,d=0,e=0,f=0,g=0,h=0):\n",
    "    a = a[ib:ie]\n",
    "    inds = a.argsort()\n",
    "    a = np.copy(a[inds]) # deepcopy forces pass to outside scope\n",
    "    if(iperm == 1):\n",
    "        return a\n",
    "    b_slice = b[ib:ie]\n",
    "    b = b_slice[inds]    \n",
    "    if iperm == 2:\n",
    "        return a,b\n",
    "    c_slice = c[ib:ie]\n",
    "    c = c_slice[inds]    \n",
    "    if iperm == 3:\n",
    "        return a, b, c\n",
    "    d_slice = d[ib:ie]\n",
    "    d = d_slice[inds]    \n",
    "    if iperm == 4:\n",
    "        return a, b, c, d\n",
    "    e_slice = e[ib:ie]\n",
    "    e = e_slice[inds]    \n",
    "    if iperm == 5:\n",
    "        return a, b, c, d, e \n",
    "    f_slice = f[ib:ie]\n",
    "    f = f_slice[inds]\n",
    "    if iperm == 6:\n",
    "        return a, b, c, d, e, f \n",
    "    g_slice = g[ib:ie]\n",
    "    g = g_slice[inds]\n",
    "    if iperm == 7:\n",
    "        return a, b, c, d, e, f, h     \n",
    "    h_slice = h[ib:ie]\n",
    "    h = h_slice[inds]\n",
    "    return a, b, c, d, e, f, h\n",
    "\n",
    "def gauinv(p):\n",
    "    lim = 1.0e-10; p0 = -0.322232431088; p1 = -1.0; p2 = -0.342242088547\n",
    "    p3 = -0.0204231210245; p4 = -0.0000453642210148; q0 = 0.0993484626060\n",
    "    q1 = 0.588581570495; q2 = 0.531103462366; q3 = 0.103537752850; q4 = 0.0038560700634\n",
    "\n",
    "# Check for an error situation:\n",
    "    if p < lim:\n",
    "        xp = -1.0e10\n",
    "        return xp\n",
    "    if p > (1.0-lim):\n",
    "        xp =  1.0e10\n",
    "        return xp    \n",
    "\n",
    "# Get k for an error situation:\n",
    "    pp = p\n",
    "    if p > 0.5: pp = 1 - pp\n",
    "    xp   = 0.0\n",
    "    if p == 0.5: \n",
    "        return xp\n",
    "\n",
    "# Approximate the function:\n",
    "    y  = np.sqrt(np.log(1.0/(pp*pp)))\n",
    "    xp = float(y + ((((y*p4+p3)*y+p2)*y+p1)*y+p0) /\n",
    "            ((((y*q4+q3)*y+q2)*y+q1)*y+q0) )\n",
    "    if float(p) == float(pp): \n",
    "        xp = -xp\n",
    "    return xp\n",
    "\n",
    "def gcum(x):\n",
    "    z = x\n",
    "    if z < 0:  \n",
    "        z = -z\n",
    "    t= 1./(1.+ 0.2316419*z)\n",
    "    gcum = t*(0.31938153   + t*(-0.356563782 + t*(1.781477937 +\n",
    "           t*(-1.821255978 + t*1.330274429))))\n",
    "    e2= 0.0\n",
    "    \n",
    "# standard deviations out gets treated as infinity:\n",
    "    if z <= 6: \n",
    "        e2 = np.exp(-z*z/2.0)*0.3989422803\n",
    "    gcum = 1.0- e2 * gcum\n",
    "    if x >= 0.0: \n",
    "        return gcum\n",
    "    gcum = 1.0 - gcum\n",
    "    return gcum\n",
    "\n",
    "def dpowint(xlow,xhigh,ylow,yhigh,xval,pwr):\n",
    "    EPSLON = 1.0e-20\n",
    "    if (xhigh-xlow) < EPSLON:\n",
    "        dpowint = (yhigh+ylow)/2.0\n",
    "    else:\n",
    "        dpowint = ylow + (yhigh-ylow)*(((xval-xlow)/(xhigh-xlow))**pwr)\n",
    "    return dpowint\n",
    "\n",
    "@jit(nopython=True) # all NumPy array operations included in this function for precompile with NumBa\n",
    "def setup_rotmat(c0,nst,it,cc,ang,PMX):\n",
    "    DTOR=3.14159265/180.0; EPSLON=0.000000; PI=3.141593\n",
    "# The first time around, re-initialize the cosine matrix for the\n",
    "# variogram structures:\n",
    "    rotmat = np.zeros((4,nst))\n",
    "    maxcov = c0\n",
    "    for js in range(0,nst):\n",
    "        azmuth = (90.0-ang[js])*DTOR\n",
    "        rotmat[0,js] =  math.cos(azmuth)\n",
    "        rotmat[1,js] =  math.sin(azmuth)\n",
    "        rotmat[2,js] = -1*math.sin(azmuth)\n",
    "        rotmat[3,js] =  math.cos(azmuth)\n",
    "        if it[js] == 4:\n",
    "            maxcov = maxcov + PMX\n",
    "        else:\n",
    "            maxcov = maxcov + cc[js]\n",
    "    return rotmat, maxcov\n",
    "     \n",
    "@jit(nopython=True) # all NumPy array operations included in this function for precompile with NumBa\n",
    "def cova2(x1,y1,x2,y2,nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov):\n",
    "    DTOR=3.14159265/180.0; EPSLON=0.000000; PI=3.141593\n",
    "                      \n",
    "# Check for very small distance:\n",
    "    dx = x2-x1\n",
    "    dy = y2-y1\n",
    "#    print(dx,dy)\n",
    "    if (dx*dx+dy*dy) < EPSLON:\n",
    "        cova2 = maxcov\n",
    "        return cova2\n",
    "\n",
    "# Non-zero distance, loop over all the structures:\n",
    "    cova2 = 0.0\n",
    "    for js in range(0,nst):\n",
    "#        print(js)\n",
    "#        print(rotmat)\n",
    "# Compute the appropriate structural distance:\n",
    "        dx1 = (dx*rotmat[0,js] + dy*rotmat[1,js])\n",
    "        dy1 = (dx*rotmat[2,js] + dy*rotmat[3,js])/anis[js]\n",
    "        h   = math.sqrt(max((dx1*dx1+dy1*dy1),0.0))\n",
    "        if it[js] == 1:\n",
    "\n",
    "# Spherical model:\n",
    "            hr = h/aa[js]\n",
    "            if hr < 1.0: \n",
    "                cova2 = cova2 + cc[js]*(1.-hr*(1.5-.5*hr*hr))\n",
    "            elif it[js] == 2:\n",
    "                \n",
    "# Exponential model:\n",
    "                cova2 = cova2 + cc[js]*np.exp(-3.0*h/aa[js])\n",
    "            elif it[js] == 3:\n",
    "\n",
    "# Gaussian model:\n",
    "                hh=-3.0*(h*h)/(aa[js]*aa[js])\n",
    "                cova2 = cova2 +cc[js]*np.exp(hh)\n",
    "            elif it[js] == 4:\n",
    "\n",
    "# Power model:\n",
    "                cov1  = PMX - cc[js]*(h**aa[js])\n",
    "                cova2 = cova2 + cov1      \n",
    "    return cova2\n",
    "\n",
    "def ksol_numpy(neq,a,r):    # using Numpy methods\n",
    "    a = a[0:neq*neq]             # trim the array\n",
    "    a = np.reshape(a,(neq,neq))  # reshape to 2D\n",
    "    ainv = linalg.inv(a)         # invert matrix\n",
    "    r = r[0:neq]                 # trim the array\n",
    "    s = np.matmul(ainv,r)        # matrix multiplication\n",
    "    return s\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the KB2D program translated to Python. I have applied Numba to speedup the covariance calculaiton.  I will look for additional speedups over time.  100 x 100 grid mesh will krige in about 10 seconds with 50 max data per estimate of 200 data in total on a modern desktop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math                        # for trig and constants\n",
    "import scipy.spatial as sp         # for fast nearest nearbour search\n",
    "#from numba import jit             # for precompile speed up \n",
    "# GSLIB's KB2D program (Deutsch and Journel, 1998) converted from the original Fortran to Python \n",
    "# translated by Michael Pyrcz, the University of Texas at Austin (Jan, 2019)\n",
    "\n",
    "def kb2d(df,xcol,ycol,vcol,tmin,tmax,nx,xmn,xsiz,ny,ymn,ysiz,nxdis,nydis,\n",
    "         ndmin,ndmax,radius,ktype,skmean,vario): \n",
    "# Constants\n",
    "    UNEST = -999.\n",
    "    EPSLON = 1.0e-10\n",
    "    VERSION = 2.907\n",
    "    first = True\n",
    "    PMX = 9999.0    \n",
    "    MAXSAM = ndmax + 1\n",
    "    MAXDIS = nxdis * nydis\n",
    "    MAXKD = MAXSAM + 1\n",
    "    MAXKRG = MAXKD * MAXKD\n",
    "    \n",
    "# load the variogram\n",
    "    nst = vario['nst']\n",
    "    cc = np.zeros(nst); aa = np.zeros(nst); it = np.zeros(nst)\n",
    "    ang = np.zeros(nst); anis = np.zeros(nst)\n",
    "    \n",
    "    c0 = vario['nug']; \n",
    "    cc[0] = vario['cc1']; it[0] = vario['it1']; ang[0] = vario['azi1']; \n",
    "    aa[0] = vario['hmaj1']; anis[0] = vario['hmin1']/vario['hmaj1'];\n",
    "    if nst == 2:\n",
    "        cc[1] = vario['cc2']; it[1] = vario['it2']; ang[1] = vario['azi2']; \n",
    "        aa[1] = vario['hmaj2']; anis[1] = vario['hmin2']/vario['hmaj2'];\n",
    "    \n",
    "# Allocate the needed memory:   \n",
    "    xdb = np.zeros(MAXDIS)\n",
    "    ydb = np.zeros(MAXDIS)\n",
    "    xa = np.zeros(MAXSAM)\n",
    "    ya = np.zeros(MAXSAM)\n",
    "    vra = np.zeros(MAXSAM)\n",
    "    dist = np.zeros(MAXSAM)\n",
    "    nums = np.zeros(MAXSAM)\n",
    "    r = np.zeros(MAXKD)\n",
    "    rr = np.zeros(MAXKD)\n",
    "    s = np.zeros(MAXKD)\n",
    "    a = np.zeros(MAXKRG)\n",
    "    kmap = np.zeros((nx,ny))\n",
    "    vmap = np.zeros((nx,ny))\n",
    "\n",
    "# Load the data\n",
    "    df_extract = df.loc[(df[vcol] >= tmin) & (df[vcol] <= tmax)]    # trim values outside tmin and tmax\n",
    "    nd = len(df_extract)\n",
    "    ndmax = min(ndmax,nd)\n",
    "    x = df_extract[xcol].values\n",
    "    y = df_extract[ycol].values\n",
    "    vr = df_extract[vcol].values\n",
    "    \n",
    "# Make a KDTree for fast search of nearest neighbours   \n",
    "    dp = list((y[i], x[i]) for i in range(0,nd))\n",
    "    data_locs = np.column_stack((y,x))\n",
    "    tree = sp.cKDTree(data_locs, leafsize=16, compact_nodes=True, copy_data=False, balanced_tree=True)\n",
    "\n",
    "# Summary statistics for the data after trimming\n",
    "    avg = vr.mean()\n",
    "    stdev = vr.std()\n",
    "    ss = stdev**2.0\n",
    "    vrmin = vr.min()\n",
    "    vrmax = vr.max()\n",
    "\n",
    "# Set up the discretization points per block.  Figure out how many\n",
    "# are needed, the spacing, and fill the xdb and ydb arrays with the\n",
    "# offsets relative to the block center (this only gets done once):\n",
    "    ndb  = nxdis * nydis\n",
    "    if ndb > MAXDIS: \n",
    "        print('ERROR KB2D: Too many discretization points ')\n",
    "        print('            Increase MAXDIS or lower n[xy]dis')\n",
    "        return kmap\n",
    "    xdis = xsiz  / max(float(nxdis),1.0)\n",
    "    ydis = ysiz  / max(float(nydis),1.0)\n",
    "    xloc = -0.5*(xsiz+xdis)\n",
    "    i    = -1   # accounting for 0 as lowest index\n",
    "    for ix in range(0,nxdis):       \n",
    "        xloc = xloc + xdis\n",
    "        yloc = -0.5*(ysiz+ydis)\n",
    "        for iy in range(0,nydis): \n",
    "            yloc = yloc + ydis\n",
    "            i = i+1\n",
    "            xdb[i] = xloc\n",
    "            ydb[i] = yloc\n",
    "\n",
    "# Initialize accumulators:\n",
    "    cbb  = 0.0\n",
    "    rad2 = radius*radius\n",
    "\n",
    "# Calculate Block Covariance. Check for point kriging.\n",
    "    rotmat, maxcov = setup_rotmat(c0,nst,it,cc,ang,PMX)\n",
    "    cov = cova2(xdb[0],ydb[0],xdb[0],ydb[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "# Keep this value to use for the unbiasedness constraint:\n",
    "    unbias = cov\n",
    "    first  = False\n",
    "    if ndb <= 1:\n",
    "        cbb = cov\n",
    "    else:\n",
    "        for i in range(0,ndb): \n",
    "            for j in range(0,ndb): \n",
    "                cov = cova2(xdb[i],ydb[i],xdb[j],ydb[j],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "            if i == j: \n",
    "                cov = cov - c0\n",
    "            cbb = cbb + cov\n",
    "        cbb = cbb/real(ndb*ndb)\n",
    "\n",
    "# MAIN LOOP OVER ALL THE BLOCKS IN THE GRID:\n",
    "    nk = 0\n",
    "    ak = 0.0\n",
    "    vk = 0.0\n",
    "    for iy in range(0,ny):\n",
    "        yloc = ymn + (iy-0)*ysiz  \n",
    "        for ix in range(0,nx):\n",
    "            xloc = xmn + (ix-0)*xsiz\n",
    "            current_node = (yloc,xloc)\n",
    "        \n",
    "# Find the nearest samples within each octant: First initialize\n",
    "# the counter arrays:\n",
    "            na = -1   # accounting for 0 as first index\n",
    "            dist.fill(1.0e+20)\n",
    "            nums.fill(-1)\n",
    "            dist, nums = tree.query(current_node,ndmax) # use kd tree for fast nearest data search\n",
    "            na = len(dist) - 1\n",
    "\n",
    "# Is there enough samples?\n",
    "            if na + 1 < ndmin:   # accounting for min index of 0\n",
    "                est  = UNEST\n",
    "                estv = UNEST\n",
    "                print('UNEST at ' + str(ix) + ',' + str(iy))\n",
    "            else:\n",
    "\n",
    "# Put coordinates and values of neighborhood samples into xa,ya,vra:\n",
    "                for ia in range(0,na+1):\n",
    "                    jj = int(nums[ia])\n",
    "                    xa[ia]  = x[jj]\n",
    "                    ya[ia]  = y[jj]\n",
    "                    vra[ia] = vr[jj]\n",
    "                    \n",
    "# Handle the situation of only one sample:\n",
    "                if na == 0:  # accounting for min index of 0 - one sample case na = 0\n",
    "                    cb1 = cova2(xa[0],ya[0],xa[0],ya[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                    xx  = xa[0] - xloc\n",
    "                    yy  = ya[0] - yloc\n",
    "\n",
    "# Establish Right Hand Side Covariance:\n",
    "                    if ndb <= 1:\n",
    "                        cb = cova2(xx,yy,xdb[0],ydb[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                    else:\n",
    "                        cb  = 0.0\n",
    "                        for i in range(0,ndb):                  \n",
    "                            cb = cb + cova2(xx,yy,xdb[i],ydb[i],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                            dx = xx - xdb(i)\n",
    "                            dy = yy - ydb(i)\n",
    "                            if (dx*dx+dy*dy) < EPSLON:\n",
    "                                cb = cb - c0\n",
    "                            cb = cb / real(ndb)\n",
    "                    if ktype == 0:\n",
    "                        s[0] = cb/cbb\n",
    "                        est  = s[0]*vra[0] + (1.0-s[0])*skmean\n",
    "                        estv = cbb - s[0] * cb\n",
    "                    else:\n",
    "                        est  = vra[0]\n",
    "                        estv = cbb - 2.0*cb + cb1\n",
    "                else:\n",
    "\n",
    "# Solve the Kriging System with more than one sample:\n",
    "                    neq = na + 1 + ktype # accounting for first index of 0\n",
    "                    nn  = (neq + 1)*neq/2\n",
    "\n",
    "# Set up kriging matrices:\n",
    "                    iin=-1 # accounting for first index of 0\n",
    "                    for j in range(0,na+1):\n",
    "\n",
    "# Establish Left Hand Side Covariance Matrix:\n",
    "                        for i in range(0,na+1):  # was j - want full matrix                    \n",
    "                            iin = iin + 1\n",
    "                            a[iin] = cova2(xa[i],ya[i],xa[j],ya[j],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                        xx = xa[j] - xloc\n",
    "                        yy = ya[j] - yloc\n",
    "\n",
    "# Establish Right Hand Side Covariance:\n",
    "                        if ndb <= 1:\n",
    "                            cb = cova2(xx,yy,xdb[0],ydb[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                        else:\n",
    "                            cb  = 0.0\n",
    "                            for j1 in range(0,ndb):    \n",
    "                                cb = cb + cova2(xx,yy,xdb[j1],ydb[j1],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                                dx = xx - xdb[j1]\n",
    "                                dy = yy - ydb[j1]\n",
    "                                if (dx*dx+dy*dy) < EPSLON:\n",
    "                                    cb = cb - c0\n",
    "                            cb = cb / real(ndb)\n",
    "                        r[j]  = cb\n",
    "                        rr[j] = r[j]\n",
    "\n",
    "# Set the unbiasedness constraint:\n",
    "                    if ktype == 1:\n",
    "                        for i in range(0,na+1):\n",
    "                            iin = iin + 1\n",
    "                            a[iin] = unbias\n",
    "                        iin      = iin + 1\n",
    "                        a[iin]   = 0.0\n",
    "                        r[neq]  = unbias\n",
    "                        rr[neq] = r[neq]\n",
    "\n",
    "# Solve the Kriging System:\n",
    "                    s = ksol_numpy(neq,a,r)\n",
    "                    ising = 0 # need to figure this out\n",
    "\n",
    "# Write a warning if the matrix is singular:\n",
    "                    if ising != 0:\n",
    "                        print('WARNING KB2D: singular matrix')\n",
    "                        print('              for block' + str(ix) + ',' + str(iy)+ ' ')\n",
    "                        est  = UNEST\n",
    "                        estv = UNEST\n",
    "                    else:\n",
    "\n",
    "# Compute the estimate and the kriging variance:\n",
    "                        est  = 0.0\n",
    "                        estv = cbb\n",
    "                        sumw = 0.0\n",
    "                        if ktype == 1: \n",
    "                            estv = estv - real(s[na+1])*unbias\n",
    "                        for i in range(0,na+1):                          \n",
    "                            sumw = sumw + s[i]\n",
    "                            est  = est  + s[i]*vra[i]\n",
    "                            estv = estv - s[i]*rr[i]\n",
    "                        if ktype == 0: \n",
    "                            est = est + (1.0-sumw)*skmean\n",
    "            kmap[ny-iy-1,ix] = est\n",
    "            vmap[ny-iy-1,ix] = estv\n",
    "            if est > UNEST:\n",
    "                nk = nk + 1\n",
    "                ak = ak + est\n",
    "                vk = vk + est*est\n",
    "\n",
    "# END OF MAIN LOOP OVER ALL THE BLOCKS:\n",
    "\n",
    "    if nk >= 1:\n",
    "        ak = ak / float(nk)\n",
    "        vk = vk/float(nk) - ak*ak\n",
    "        print('  Estimated   ' + str(nk) + ' blocks ')\n",
    "        print('      average   ' + str(ak) + '  variance  ' + str(vk))\n",
    "\n",
    "    return kmap, vmap\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a simple test of the KB2D code with visualizations to check the results including the data, estimates and kriging variance plotted.  \n",
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE337\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will have to update the part in quotes with your own working directory and the format is different on a Mac (e.g. \"~/PGE\"). \n",
    "\n",
    "##### Make a Simple, Small Example\n",
    "\n",
    "The following are the basic parameters for the demonstration.  This includes the number of cells in the 2D regular grid, the cell size (step) and the x and y min and max along with the color scheme.\n",
    "\n",
    "Then simply assume some data, and a variogram model.\n",
    "\n",
    "This will allow for very fast iteration with the kB2D program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Estimated   10000 blocks \n",
      "      average   1.984891698010888  variance  0.01224913076801526\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "nx = 100; ny = 100; xsiz = .05; ysiz = .05; xmn = 0.5; ymn = 0.5; nxdis = 1; nydis = 1\n",
    "ndmin = 0; ndmax = 10; radius = 3; ktype = 0; skmean = 2\n",
    "vario = make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=45,hmaj1=0.6,hmin1=.2)\n",
    "tmin = -999; tmax = 999\n",
    "x = [5,4,2,1,1,3,5,2]; y = [1,2,2,3,4,4,2,3]; vr = [1,3,0,1,2.5,1.0,1.3,1.2]\n",
    "df = pd.DataFrame({'x':x,'y':y,'vr':vr})\n",
    "\n",
    "kmap, vmap = kb2d(df,'x','y','vr',tmin,tmax,nx,xmn,xsiz,ny,ymn,ysiz,nxdis,nydis,\n",
    "         ndmin,ndmax,radius,ktype,skmean,vario)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the resulting kriging estimates with the data plotted and the kriging variance map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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38LUDMztpZu83s8+b2R1m9lMdtnmmmV0ys0+mXy/fp7MSEREZbr3W5xGZAEcdRRGRAvD+XLFsAj/n7reb2QzwcTN7r7t/rm27v3P3b+/HAYiIiAyzPtXnoaCgKCIyaEEVJh/ew44f2/ZZd38AeCD9fsnMPg+cANqDooiIiLTruT7DTjV6GCgoiogMWrwOy1/u61uY2Y3AzcBHOzz9NDP7FHA/8DJ3v6OvByMiIjIMDqA+F5mCoojIoLn1erP8vJndlvv5Fne/pX0jM5sG/gz4aXdfbHv6duAGd182s+cB7wAe2cvBiIiIjJTe6/NIUFAUESmC3m58X3D3J223gZmVSULiH7r729qfzwdHd3+Xmf2Omc27+0JPRyQiIjJKRmRiml4oKIqIFEEfrliamQG/C3ze3V/ZZZurgYfc3c3sKSSzYZ/b94MREREZRuooiojIQPWnED0d+H7gM2b2yfSxXwKuB3D31wIvAH7MzJrAKvBCd/d+HIyIiMjQUVAUEZGBcSDe/0Lk7h8Ctn1hd3818Op9f3MREZFh16f6PCwUFEVEimCMr1iKiIgU1hjX52DQByAiIuC+9y8RERHpr17q825qtJk918y+aGZ3mtkvdHj+ejN7v5l9wsw+nc5MfqDUURQRGbSgCtM39bDjh/f9UERERCTVc32G7Wq0mYXAa4DnAKeBj5nZre7+udxmvwz8sbv/LzN7LPAu4MYeD6YnCooiIoMW1WHx7kEfhYiIiOT1rz4/BbjT3b8MYGZvAZ4P5IOiA7Pp93PA/f04kO0oKF4BC3e/sIpHGuUrItsY43sgRERECqv3+jxvZrflfr7F3W9Jvz8BnMo9dxp4atv+vwa8x8x+EpgCnt3rgfRKQbEHewmI+X0UFkWkqzGeVU1ERKSweq/PC+7+pC7PdXrR9jsbXwS8wd3/u5k9Dfh9M3u8u+89iPRIQXGPegmJ+X0VFkWkI3UURUREiqc/9fk0cDL383VsHVr6w8BzAdz9H8ysBswDZ/pxQJ0otezAwnjTl4jIfnMHj23PXyIiItI/vdbnXdTojwGPNLOHmVkFeCFwa9s2XwGeBWBmjwFqwNl9PsVtqaO4DQVDETkwWu5CRESkePpQn929aWYvBf4aCIHfc/c7zOwVwG3ufivwc8D/MbOfSY/iB90PdnEsBcUu9hoSg1JM3FSDdpTEUdj1uSCMDvBIZPSZhp6KiIgUTv/qs7u/i2TJi/xjL899/zng6X15811SUEztNhgGpe7bKSyOhu0CYn4bhUXZN44msxERESmaMa/PCorsHBK3C4fdtlVgHC75cOhR918IFmp8oPRBWIG5Gwd9FCIiIpI35vV57IPiXkNity7SbrpQUkzZZ5cFxG6fZRBGeGQKi7L/ojp+8d5BH4WIiIjkjXl9HuuguF1IzAfE3QwxDMJIYXHIdAuIcYclTIIwbg03VViUvhjjoS0i+8W6LEHlmpxuZHT7jEGfs/TJGNfnsQ2K3UJit4C4XajM1kZUWBwOnQJiFg7bQ2KQfu5xFLS+V0iUfedoMhuRK5CFh6C+8f9RXPFNzytEDLd8QNTnLAdmzOvzWAbFnUJiFhDbt8uHyPw9iBbGrbAoxZYPiVlAbIXEZrchpyo60n8HO+G1yPBrD4dBw9k8j316MbCi/7mGVadw2Olz1mcs/TTO9XnsgmKnkLhdQOw2kY1mOB0+cRR2DYhxOqwgboYEpdxQ4zQ8KixK341xIRLZq3xIDBqOdbhDJMCJy0ZQT4KEuk3Dpf0zhu6fs8Ki9NUY/6c1VkFxLyExe7xb99GjYM9hUV3HwejWRcwCYvLP3GfTZHNY7EBLY8j+srG+B0Jkt7YGRE/+2dzYxtO/bGKsFRZleOzpM9ZnK3033vV5bILiTiGxW0C0XGDwLkMTd0MhcTA6dRHbA+KWsF9iV2FRZN84+BjfAyGyGxYFrfAQrsVYE6ytm+ghgLWCRJ66icWX/4zzATFY29zSiWvJZxw0nBiD8oAOWEbfmNfnsQiK3e41zHcR8wHRugQEK0XbhsXWrJltwUMhcTA6hcRmvbQpIEZRiKfdRAtyQ44r+UmN4vQrwkJXN1H6Y4wLkchOLAoorQA44ZoTrDrBmmGNALK1b0PHy06cDkUMcKJQ/18Ni80XAjYCojUMW8v97RU6ELfCYp4uBkhfjHF9HvmguF1IzHcR8wHRutyX6M2gY1hUECyefEjMwmGzXtoUEJv1jf/8w1KMx8GmsAjqKsoBKVXh0I2DPgqRwmkPD62AuBZiawE0Aignv7c9dIwYKzte3hiWGFdcAaLgsgsBFjnhqhOsOcHlANZCLLLW5+zprOPWSJap8pKlXWSRPhnz+jzSQXGvITELiO1hAUhCRCnGd7gnURPcDNZ2XcRmvdQKiHGchMUNDcK2CwRBkBQkdROl75rr+IXxXdBXpJN8eAgaTukCrYDoyxW8EUAUQK2JTdexyFrLHmWSsDjGM1EUXKcLAeFSGhCXy/haqXUx3ubWsLRfTC3ZP3+Poi4GSF+MeX0e2aC4U0js1EXMAqKVN/b1xu6Cn9ZPHLz2kNhcLxPHRrNeJm4GNBtlGmlYbO8Cx2FAyMbFg6AUJV9aO1EOyhjfLC/SbiMkQrjqhEsQXCzBaglfqRCvVPBGOit1NZnlxKbrrf29hDpNBZeFxHAtuRAQLjvBUogtlfDlCvFylXg9+TPVyhEBYFN1mG4A4FnHuKwZT6XPxrg+j2RQ3EtIzAJiFg63dBPL3cNi3Aw2BY5O3UQNSz0Y24XExnqZZr1Eo1HGo4AoSrqJYRgRtv23EoYRQRBv6SYm36ubKH3iNtb3QIjkZSGx1WG6mIaHSzWiixNEq5Vku9bF3Si5VxHwWoyHaXhIA4Q6TcWTD4nhqlM6b9hKEhCjc5NEy1Xieomgkl4EiAOs2kxWxiwn96Jmw04VEqWvxrw+j1xQ7DUkdhpu2q7bsNNu3USFxIPRKSQ266XkK+0iNutloiigUS9vDDlN/tagVGkSBMl/A+3dRA05lYPgjPeCviLQucMUni23wkPj/BSNxQma62VK1QaVIyubarenk9l4WQGiyDYuBCQz15bOBtiFCvGlGs1zU9QvTBHVy0TrJSbmlwgn1zd2LsVbLgaALgZI/4x7fR6poLjXkBhUm5uvSAaOx9YaztKJN5Nhi1k3UTOdDtZuQuL6apUoCmnUy631EoMgJo6CtKsYEZbiTd3EbKZTDTmVAzPGQ1tE2jtMyVDTMr4wSfP8FOtnZlg9N0OzXqa5XqYyuU4chdSOLSadpuk6XktmwowmFCCKqlO3mIUa8YUJ6gvTrJ6dZe3SFHHub6iJeSgfXsGqTbwWJTPbTmxcDNBnLH03xvV5ZIJiryExC4gAlCOsEUI56hoWs5CYpwlsBmO7kLi+WqXRSIec1ss0GmXW18tUq43W/kEYb+kmlirNTd3EZDt1E6X/xnmdJhlvHUPi2QrxuUkaZ2e4/MAhLp+fYW15orVPZXKdsNIgnKinITEinow3DUdUgCiWLSHxbAkWJojOTbF632Eun5vl8sWp1gXdSq3e+rstPLSKTdeJZ6LkYkDNNFGRHJhxrs8jERSvKCSmN8G3tIVFbwR4HODNYNOyGPluohy83YbEtdUajXqZeqNElH5+1WqDMIyoVJJ/lipNytUGpUpDQ05lMJyxvgdCxlfHe9UWqsQLU6w/OMfiqXkWz8yxvDiNRwFzxy4CSX0Pq02C6fW00xQrQBRYe0gsPbjRLV6+Z57FBw+zdG6W5cVpjl51rnUbSKnaIJyqY1N1fKZJXHOiiUD3n8rBGfP63PegaGYhcBtwn7t/+76+dodfEHsNiV94cIW/vG2R1XrM1z96mmc84jBGMvw0W4g9C4m7GXIq/bebkLi+WqVRL3N6cZ2/O/tpzteXuWnqap5x/CaCAMqVJBiWK80tQ05LlYZCohw4H+OhLTKeuoXE6Mw06w8c4tKpec6dnufCwmEWL00TliJKlQZHTywwcXSJ8uEVgrk1fKZJNBPTnFaAKKJ8SGzdd7owSeOhGZa/Ms+F++Y5/+ARLpyfY2VlEoBjJ84yMbdC5cgKpSMr+HQj6SZqyKkMwDjX54PoKP4U8Hlgdj9f9EpCYlBtQjnizz95lr/+xBIvftZhpidCbv3IIu//7DK/+vzrky5i2k0ENoXETLeQqPsT+2fHexJzIfGOc+f5w1Mf5mnVp3Nj6Sh3Ln+J31q5lV98/LMpVWKCIKZUblCqNJOvaqO1HIbIgSpVCY5eP+ijkDHTzwu5u9G1k/jAIS7cc5xz981z710nue+hwxyevcwNN97P1NwKk0eWqB5fIjyaBIjoUERzViGxiLYON90IiYt3X8WF00d58NTV3HnXCdYbJR510/1Mzy4zfWSJifklyseWsLk14pmIaDrpGDentGaiHKAxr899DYpmdh3wbcB/An523153m18Q285umnYSAVajJn/y9xd5/c+cpBImwe5x10/wK296kE/ctcbXXF3rOOQUkrCipTAOXn6ob6eQuLZabd2TuLpa5U/u+wj/Yup7OFKaIwicE9VjfGS1xAcvfJZ/cfiRGwGx7b5EdRPlwDXrxAunBn0UMn76ciF3N8LVkKCRzm66BHaxTLwwxdp9h1sh8a4v3ch9Dx6iVIqZm1veCBDHFikdWcEOrxEdaarLVFCd7kn0C7UtIfH2T99EtRIxVaszM7fEzNFFJo8uUjm+mHSMDzWIZjZmOXXVZzlIY16f+51s/gfw74B9+83dLSQGpXjLH/dblsDIJq4pR3zu3nWe+PDJVkj0dJ2U59w8wz/evbQpJHYactpOIfFgeJSsjRhHAXFsxHGQWwJj457E5TUnduNoGhIDi7HA+aqJx/CZ8w9SqjSpTqy3lsIoVZoKiTJQ7rbnL5Fe5S7kvu6g3zsLieGaU7qwMXHN+oNzyXDT++b50udu4qGFWc4ulzl5zTmuu+F+jt1whtmTC1SuuZR0mQ411WUqqCwkWsSmiWuaZ9PhpqePcv+913LHF27g/OUS1XKTf/aYu5m/doFD151j8sYFgqOX8cN1mkecaCK9/1RLnsgA9FKfR6VG962jaGbfDpxx94+b2TO32e4lwEsArpud3P41twmJ+W2ybiLQdZ3Ew1MhD15obnyQbuDw0IUms6WJPd2XqJDYf1uGnKZrIzbWN9ZJzJa/qDdKBB5S93XcIkIDCxwzZ9EvcWiiTLnaaAVEhUQZOGesp9+Wgcgu5M4c5JtaFLRCYrjsyRIYl2o0zs6w8tAhLp05xIWFwyxcnAbgKY+5nxsefoqj1y0we3KB6tWXCI5eJj5Wp3kYhcQCyu49tSidxfZiCJeqxIs11h6YY+nsHBcXDnH2zGEAHnH1UiskHrnxDFPXJyGR+TWax+JNIVGfsxy4Ma/P/Uw4Twe+08zuAd4CfJOZ/UH7Ru5+i7s/yd2fdHSyuuc32em+xG5uuqrG0nLMP35xpRUSz1xo8vYPLfKcRx1TSCyQfEhs1kvEzZC4GdJIh55GUUi9XiZKP6MgiKlVIx5/6Bo+uvYRsBgzp8E67199H9/5qBOUKg2FRCkU971/ifQifyF3h+1eYma3mdlt585e+e/HjQCRdJmCpRBfrtA8P8Xa2VlWzk+zdGGGxUvTnLjqAo951CluevS9HLvhDIduPJOExPkV4mP1tqGICg9FEtQtuRiQLnViSyX8wgTrDxzi8rlZls7NsnQpuT5x0w0P8rjH38Xx6x9qhcTw6ArMrRMdivASCokycL3U51Gp0X3rKLr7LwK/CJB2FF/m7t/Xj/fKQmLepiGnbd1E0jUSX/FdN/If//wUv/vui0zXAs6cj/m5ZzycuXJNIbEgWv/u005i3Axb9yV6HCRDT+uldChq8nlkayV+z/VP5K33f5jXL36aQ+EsFzjL9zzuBr766jmFRCkUZ7zXaZIDl13IfR5QA2bN7A/aa7S73wLcAnDzE2tX9GdPFhKDhlNajAkvhtiFCtG5SdbPzHD53AzNepnqxDqzc8vMzC4zc3iRQ9ecZ+b6BSrzywRXL7VCYtZliib0u7tI8sOKwyUILpbwSzXqC9NcXphl+fwM1Yl1ahNrXHf9ZaZnl5k7dolDJ88yceJCEhKvuUx0KJnhtDETKCTKQI39fDtuAAAgAElEQVR7fR7qdRSD0tb1E7NuYifeCHFoTWhzpFrjv7/g0SxciFitwzVTNYhKCokFk3QTg033JUZRSNQMWl3EvCCIKadrJP7o455InRVWbIWThx9BpeIKiVI8bmM9tEUO1kFeyM3LAkSwZthagK9UaC5O0LhcpbleBmBqboVKrc7U3ApTRxeZuu485WNLreGm+ZCo+9WKxdK/i7KOsa2FsFoiujhBc6VKVE/+5AxKMVdd/xBBEDN9dJGJo0tMnDzfGm6azWKrTqIUwpjX5wMJiu7+AeADV/Ia7R3DTkNOd308aUcxWwLjyGQJr+iexKJpvy8xG3IaN4NWNzH7LIIwpkyj9X0lt07idNk5XqnonkQptHG+YimjLX/PWhYgfLlCvFIhXisT1cvEsVGqNAgCZ+7qC9QOLVM9vpTMbjp/mehYg7hmul+toPId43A1vRiwVCK+VCNarSSfcRRQqiR1ujJRZ/LoErVji5QPXSaYX8Hn19OJaxQSpVjGuT4PRUex6yQ27bOcdugmeiOA8tZ9N62RqIlrCqd9yCnQtZsYhjFhGBNFAWGYdJXL5QZhafM6iZrdVIptfAuRDM5+XMjdTtZlytZLbAWI5Qrxepmg1qB2aBmPjLDSJKw2qR5epnxkhWB2DeZXiY5snt0UFCCKJPuMNzrGuYsBy1UsiKnMrBI3A8oTdcJKg/Lk+saFgHQJjPbZTfUZS3GMb30ufFDsFBLbZzndsk9bd9EbAVaOW98DHZe/ABQSC8Sj5H/MTt3ETkqVJmEYUa40sSCmXG20rlCXqg2tkyiFZaUKwfzJQR+GSF8knaYYazjWCCBXWy2IKc+tJktcVRuEU3XC2dVkaYSZZuteNc1uWmxZx9iaYI10aHHu+XBynYlShDdDglqDUvoZ23Sd+FCT5uFkciKFRCmaca/PhQ+Ku7GbYaftARFQSCyojkNO025iJizFQIM43HgsCOJWWMw6iAqJMgy8WSc6c3rfX9fMTgJvAq4mWc/2Fnd/Vds2BrwKeB5wGfhBd7993w9Gxk5+OKI1IVgzWNu4ZcCCOBnx04DK/BLB9Do2XW+Fh+R+xAAPUUgsuKDhyeecXQxoBBAFyfrVae0Oy3XCiTqWfs4+06SZuxAQlxUSpXj6VZ+HxdAFxfy9id1kQbD97PIBEdg01BRQSCyguMO/ewtiPA6SsNhMQqMFMWEYJVel08AYlCKCMG7dE6GQKIXWn3sgmsDPufvtZjYDfNzM3uvun8tt863AI9OvpwL/K/2nyBVphcSIjQAB2HQ9eT63rU3XsWoz6SLORETTpg7TEMjff9q6GBAZlGNsbo0AsGoz2bYUY1N1fLqB12KimZjmbP5CgOqzFJTuURxOnYadejNs3afo7YGvLSC2vu8SELPn5eC0dxNbjzc3z26aLXlSqmwOiN26iLD9xQWRgerTmkvu/gDwQPr9kpl9HjgB5IPi84E3ubsDHzGzQ2Z2TbqvSE8s9/s7aCT/cXvZsciT2cen6xs1vBTjM03iWkxcc3WYhkz2+UL6Ga+Bh46RhEVrBslnXIvwWkw8GacTE6lbLENghNZE7EWhg2K3SWx24s2tSybARujbTRcxv70MXlCKkt5Iqf3xzQEx6yJqqKkME8f6Pquamd0I3Ax8tO2pE8Cp3M+n08cUFOWKWadfv+UkLHK4DqHjZc8FxCAXEPW7u8iybiK0/RWdXpx1gDCCchOvxennvBEQdSFAhsFB1OciK2xQ3G4Sm/Y//ONmQFCK8SjYsp9v6koFmx5TSCyWuMOaiHlZWAwq8abHugVEUBdRhkhvhWjezG7L/XxLukj6JmY2DfwZ8NPuvtj+dKej6eVgRGBzNzETTySBIiD9/V1Luk8egpcNL6mLOOy8BHHNgBhrGNTSx9PPOd8pBvQ5y/BQUCyW3XYS88EwHxbb7SUg5reTg5fNdNpNkFv+JB8QAYVEGWre24K+C+7+pO02MLMySUj8Q3d/W4dNTgP5Kd2uA+7v5WBE2nkIMcm9illYhI1wmIWG7F5ELX0xHDpdDICNsJjV4fznrC6iDKse6/NIKGRQ7NVugp9C4nDJQiBtw4mzwJjvIgIKiTKcvD8L+qYzmv4u8Hl3f2WXzW4FXmpmbyGZxOaS7k+U/RaXN8JiFg7zwUEBcbhlFwQAAnxTOAR9zjLE+lSfh8VQBsU4CltBoNNw0+zx9n2ge0DstI8cnO2GnQZtn297BxEUEGUE9KcQPR34fuAzZvbJ9LFfAq4HcPfXAu8iWRrjTpLlMX6oHwci4ycJBka2ol4Ubv5vfCM8aDKTYdX6jBveCoX5z1kBUUaCguJw2y7g5QOIQuJwyQJhHAWbwmHyTwVEGR1WrhAev27fX9fdP0TnexDz2zjwE/v+5iJsBImtj4HCw/DyMG4NP+30GW88rs9Yhlu/6vOwGNqgmAXATgGhvTu1XUAEhcQiCMKIOAqx0PHIWj8nz8WbtgMFRBkt3qjTfGh8F/SV0ZIPEZAPhhvPy2hp/4xBn7OMhnGvz0MVFLMJazY9ts2QxZ0CIigkFlE+LLY/nlFAlFHj+ptKRkgWEvKBUcFhtHT6jPOPi4yKca7PQxUUoXNYbH9+txQSiyXfRcyHwvZtREaO21jfAyGjS6Fh9OkzlpE25vV56IIi7C0M5ikYFl8+LLY/LjKqnPGeVU1ERKSIxr0+D2VQ7IVC4vBQKJRxNM6FSEREpKjGuT6PfFBUQBSRoTDGhUhERKSwxrg+FzIodlsbcbf7iogMFQePx7cQiYiIFNKY1+dCBkXoLSwqJIrIcBrvm+VFRESKabzrc2GDIuwtLCokisiwsnKZ0tXXDvowREREJGeU6rOZLQJGMkfPJLCafg8w5e5bZpMsdFAEBUARGX3eaNB44P5BH4aIiIjkjFJ9dvfZ7Hszu93dvzb/c6d9lMJERArA3fb8JSIiIv3VS33eTY02s+ea2RfN7E4z+4Uu23y3mX3OzO4wsz/az/Mys3zDsNxpm8J3FEVERt24r9MkIiJSRP2qz2YWAq8BngOcBj5mZre6++dy2zwS+EXg6e5+wcyO7+MhfBD4UzP7K+AbgM912kgdRRGRQXOSm+X3+iUiIiL902t93rlGPwW4092/7O514C3A89u2+RHgNe5+AcDdz+zjmb0M+Cvgq4DbgBd32kgdRRGRIlDwExERKZ7+1OcTwKncz6eBp7Zt8ygAM/swEAK/5u7vvpI3NbObgWeQROAPufv/3m57dRRFRAauP/c/iIiIyJXorT6nNXrezG7Lfb1k0wtv5W0/l4BHAs8EXgS8zswO9XwmZj8NvB44DBwBXm9mP7vdPuooiogUwCgEv16m3hYRESmyK6jPC+7+pC7PnQZO5n6+DmifXvU08BF3bwB3m9kXSYLjx3o8nh8GnuzuawBm9pvpa72y2w7qKIqIFID73r+Kxt1n3X0mnYL709n36c+fGvTxiYiI7FUv9XkXNfpjwCPN7GFmVgFeCNzats07gG8EMLN5kqGoX76SUyEZwpoJ2drF3EQdRRGRAbPS6Czom2dmJXdvpj92nHpbRESkqPpVn929aWYvBf6aJLD9nrvfYWavAG5z91vT577ZzD4HRMC/dfdzV/C2/wf4iJm9Pf35X6SPdaWguA/iKAnnQRgN+EhEZBglC/o+MOjD2G+7mnpbRESkqPpZn939XcC72h57ee57B342/dqP9/ufZvZBkpoM8L3uvu1oHwXFHmXhsNtjoxgaO50zjOa5ihy4ePjvUWzzMpL7IbKpt1872MMRERHpwYjUZzO7AbgIvDP/mLvf220fBcU9yocljzb/h2Ohb9puVAJUt4CYf35UzlVkEPq1oO8g7HXqbRERkaIapfpMEhCzCeemgBuALwGP7baDguIudAuH7UNOPbJNYXGYbReIM6NyriKDNxrLXaRTb/8gyQ34kEy9/UZ37zqjmoiISHGNRn0GcPevzv9sZk8mqdldKSjuIAtMWVjKB6g4CgjCeFNgHIWw2H7O+cegczBWV1HkCvjIXLHc89TbIiIihTU69XkLd/+YmW17W4iCYhedAmIcbV1NJHssC4zDHJa2C8WdthuVYCxSBCNSiPY89bbIfrIOdRrAw/iAj+TgjOM5ixykEanPAJjZ1cBTSWrzx4D/y8wsnThnCwXFNvmwlH0fR8GmkBg3Q4JSEgiDEfhFvNtQDKNxviKFNBqFaM9Tb4vsh/awFNSNuOKbnh+14DSO5ywyEKNRnzGzFwK/QTIruQP/E/gFd39zt30UFHPaQ2I+IMbNNDRmMx8180Mx49Yw1GETR+GWDmJ7MG7X6TyHuZMqUgQ+fL8+tuhl6m2RXuWDUlBP6ljQyIKSA5uD0yjIzjk7Xxj9cxYZtFGoz6lfAp7o7ucBzOwI8AFAQXEnWWDKd9PiKCBuhsSxtYIiQFCKtgTG9vBU9OC0Xec0f655WRc1H4o17FTkylm5TPnE1YM+jCvWy9TbIr3IB6YkKDnWVnaDtuA07B22refMjucsIldmVOpzzqXc9xd32njsg2K3LmI+IMZxW3etuRGahtF25wy5rmkqCPLLfmztnBY9FIsUXVxvUD/90KAPYz/seeptkb1oD0sWJfUpaDjWBM/9VRNjIxGcOp1zKyjmzjkuGxYpLIrspxGqzwB/CbzbzLIO4vcC79puh7EOivlhl816uWNAjJsBUW5SlzCMkn9rubAYhHH6FWGhFzo4tXdO80Nr2zunLaWIIPBN92aKyH4ajem3e5l6W2S3LAq2hCVrgjU2uokeQjzR+f+lYewm5s85XIu3nC9AXDO8lITluDz8v0dEimU06jOAu/+imX0H8M/Th17l7rdut8/YBsVOgalZL20JiJ7rJloQE0UhQWmj2AxLcNpL5zRuBq1zDII4CY+liFKluSUUi8j+GJVClLebqbdFdmNzYHKC1SQsWcOwtRDSeuTlZBhqt7A4TMLVcFMozs45uBxAZK1zhrgVFjPqJorsn1Gpz2b21cDfu/s7zewQcJOZBe7d78Icy6DYHhKb62Xi2JKuYhoQm/XkX00WnsqVJh4HWJALiemQzKJ3EzuFxOycu3VOAYJSTBwHBEG8afjppm0KeL4iw2iECtGept4W2Uk+MIWrTrDmSVhaC7B0VJCnocmIsXLSaaSc7D9soalbKA6WQmjYlnNOL/ESTacT05WT0d/D2EEVKaJRqc/A64FnmtkkSX2+G7gP+KFuO4xdUMyHxKyD2KyXiOOAxnq59X17aKKehMXMsCyPsd3w2nww9rb7MLP4F5TirkNsRWQfjcD/Ur1MvS3STXtgCpezgBhiy2V8rZT8b1OOsel6EpyizX/QDVtosiigtEIrFIdLYGshthbgyxVopHcg1prYRLMVFjPeeS46EbkSI1CfU4G7L5nZdwHvcvefMrNPb7fDWAXF9pCYD4rNRplGvZSEpiggigLC1syeGwUmDKNWhy0oRYXuJnYLiflg7HFA1NwcEsP80Nq0g9ppiG3RzldkaPnIXLHc89TbIp1kITFcywWmpRK2FuLLFeLlKp5OvGbV5CKuTdfToafJBC/DFpqykBiuJR3R0nnDVkJYLeErFeKVSmvbIKpjkIRF0nsz0/sT48rwBGORwhud+gzgZvY1JB3E12SPbbfDroKimT2JZF2sa4FV4LPA32R/DAyDLDRlgSkJiuVNXcRmvUy9Xt60XxYWgyDGgnhLhw0odEjsdA9mMw3E7d3TLARnsnMdliG2IsPKGalCtKept+XKjEJ9btfeVcsCk1+oES3WiJarm24FCdkIi4TeCk1x2YYmNGXDa1ud06UQWyrhl2pEFyeIVpOQmJ2zBQ61ZnK+ZcfLlguLo9P+EBm0EavPv0Ay/PR24D1mNgv83nY7bBsUzewHgX9DMob148AXgRrw9cDPm9lngV9x969c8aH3URyFRPXSpsCUD4mNRvrPennzkNP04l210iAsxZu6iaVqoxWcimanezCbjTJRM6DRKOO5BYsjNjqI+dDY3jkVkf03Igv67nnqbenNqNTndvmuWrDqhGdL2HKZ+FKNxtkZopUqjctVwkqD8tzqpnkDPHTiyXgjNA3BvYn5zmnQSEJieLaML1eIzk1SPzNLtF4iqpcpT64TTq1vhMUwTkOi46WNYCwi+2tE6jPu/h7gPbmHFoFXbbfPTh3FKeDp7r7a6UkzewLwSKCwhWgjNG0OTI31MvW1SquL2KiXN62XuBGY4i3dxI2OYvG6a9uFxGyoaSMNylEuJIZhEoSBzecaeOvexEyRzldkFATlMtXrhn9B316m3paeDX19zsvWCsxCYrjsBOdLsDBBtFijfmaW1YUZGqtV4iigVGkApGHRsVoTr8V4CNGEDcW9iZ2G1wZnq/ilGs3zU6yfmWH13EzrdpmJuRVqQGlmFcoRlGK8lsx4mg+JRT5nkWEzKvUZwMx+j2St403c/YfM7Nfd/Vfbn9s2KLr7a3Z4/pN7PsoDlB9umg9M9dVqKyytrVZpNMqsrydBsVJuEpYiwjD5KnXoJmbBqWjdtZ1CYr57GqXblNNim9lyrgW/D1NkFMSNJmsjsKBvL1NvS2+GvT7n5UNiaTmZ1TQ8W8YXJmk8NMP6wgyXz82yfH6mtc/0kSU8CgiqTWx6HZ9uEE/GxBMb3cQiB6b2kFg6b9hClfhSjfoDc6yeneXy+RnWlieA5OJ1dXoNgHB6nWBuDZ9pEtecaCIgqhU/GIsMo1Gpz6m/2Oa5v+304G7vUXwY8JPAjfl93P0793BwB2qnkLi+WqVRL7O2WmOtHtBsBoQW0gxiypVkWGmp0iQIYkrlBqVKM/lqG3JalOC0l5C4vl6i0TRKQUgQxVQqDUqVBuVKs3WuWedUQ05FDsaI3AOx56m35coMY33upLzIpq5afG6SxtkZlu49xtKZOVYuTRHHAVNzK0BSe0uTdcKJejKJTdpZi2pW+Pv0OoVEHpogvjDB+oNzXLznOJcvTbF4fpaJqVXK1UZajyPCqXWCqTpMJB3ULBiDOoki/TIi9Rl3f1v7Y2b2k+lz7+u0z25nPX0H8LvAO4Fd/SYysxrJFOnV9H3+tFNLsx/aJ67pFhIvLge8+dSHuOvyAwSEHK7W+L4bn8JsZZJKpUG5FZrijsGpqCExf875kLi2Zrz1ntv5xPn7qFiZiXLA9950M4+aOLxleG2p0kzDcqMVEotyviIjZ3RmVdvz1NtyxfZcn4vEoqAtJFaIH5yhvjDN0lfmOXf6GBcXDrF4aRqAqznD4asuUJlZozS3ih1exWeaRDMx0URQ+AlsuobEc5OsnjrCpdPznLtvnuVL0ywuTjMxsc7xa88yfWSJ2uEVSrOr2NwaPhURzdAKxro3UaRPRqc+Y2YvBX4EmM09fK2Z/SzwP9x9y/2Kuw2Ka+7+23s8nnXgm9x92czKwIfM7K/c/SN7fJ092dJZy01ckw03zTqJt9zzHm70x/Ls2edjATwQ3c9rv/wOXnHzc5isJB3FcjXptmUdxSKGxI3vt5+s5413fZSJ5jwvu+5ZhBZwtnGe1915K7/8xKdx1VS1da66L1HkYPVrVrX0foRvB864++M7PP9M4M9JOn8Ab3P3V1zBW+556m25Ynuuz4O8kLvpONKJa4JGLiSem6S+MM2lu6/i/H3znLr7Wh566AhB6DzsxvuYmFpl6ugiE1ddpHRkBebWiWeioRhyulNIvHDPVSzcP89dX7yBi0uTXDV/iaPzF5icW2by6CKV+SWCo5eHKhiLDLsRm/X0J4BvJZnEBpLT+wDwjcDlTjvsNii+ysx+lWSmnPXsQXe/vdsO7u7AcvpjOf3q6x8MndZJbJ+4Zm21ytpqja9cusxaw7l55gmYOUHgnAyv4rHRV/GxxS/xrUdu3BQSi9pJBLZd9iMLiZcux9y1eIGfOvGtBGaYOVdVD/HM2Sfx/ofu5PuPParjjK5FOl+RUdanQvQG4NXAm7bZ5u/c/dv36f32PPW2XLE912cGdCE3Lz+7abjsreGm9QfmWiHx3ruu455TxwE4ec05pueWmT66xMT8EqWjK9jcGvFMRDSdDDltThV7+GXHexIvTGwJiQsXk+7p7NwyM4eXmDqyTHV+ifDQKsytEx2KaM4GhQ/GIqNihILi/e5+T/4BM1vYbjml3QbFrwK+H/gmNoa2ePpzV2YWkkzb/QjgNe7+0Q7bvAR4CcB1s5O7PJyt2kNi3AyJm2ErMEVRmMxu2ijTjALON1Y4Gs63QmJgMRY4x0rznK/fT2VifVN3LRuCWbTQtGnIaXrOcTPA44A4DvAoWSfxYn2NI+W5Vkg0cyxwrq4d4RNrn0/uxWy7B7OI5ysymgz6UIjc/YNmduO+v3D399vz1NtyxfZcnwdxITevPSSGZ8t4OonLpbuv4uy9x7n3rpPcfeoYd18q87SHn+Pa6x/k6IkF5k4uUL36UjKZy6EGzcMbwy+9wPUqWycxaORC4sIUl+89yoV7ruLsqeP805du4IunDlMy52sfd5rj157l6HVnmbnhLOWrlrD5y0RHmq3uadGDscho6E99PkhmNuful9z9WW2PTwJ/uN2+uw2K3wXc5O71vRyYu0fAE9LZ795uZo9398+2bXMLcAvAE6450lOhyjpr+cCUdRQ9Djavk9gMieOA62pH+fPmh8AiAksWrzVz7o7+iW85NrOlu1a00LTlvsTcOUdR2BpmW087jUfLM5xtLtD0BpUgbJ3vFy7fw2Ovn9mYwEYhUeTgOXg8sEL0NDP7FHA/8DJ3v6PXF+pl6m25Yj3V591cyO2HLCRaBMFqsgSGX6jReGiGyw8eYvHMHBcWDnPqgaOUSjFf/+iz3PjwUxy//iHmrlugduICwfwKPr9O84gnQy/LRjRR3HplUZAMr02DsV2sJLObLkyzcuYQl87OsXD2MBcWJzk+t85NNzzIyYfdz5ETC8xcv0D5WBoSjzWGpnsqMjIGW5/3yz8Cj85+MLOnAD9MckHxndvtuNug+CngEHCml6Nz94tm9gHgucBnd9h8T/LDL/MTucRxsrB8Iw1P2TqJYSkiiALmpwOePH8t77j0Np459QwmqPCJtU+yPnGap5548qbuWpjem1gU3WY4jeOke5iFxSgKWmsjToTwvBOP5g1n/pxvO/J0Dpen+MTyl/in+PO85KYn5SawUUgUGYQeh7bMm9ltuZ9vSS++7dbtwA3pEMTnkUyM8sheDiS156m35Yr1VJ93cyE3P+Ln5Mnd/rnQXXaPnkVOaTEmvBhiy2Wa56ZYX5hh5dwsSxdmWbw0zeHZy8zNLXPtybSTeN0CEyfPJ5PXpCExmkhnOS3wRC75c87WhvTlCs1zU6yeTZb8WF6cZvHSNFfNLzI3u8y1N96fdBKvX6ByzSWCo5eJDzUVEkUGZASGnn7CzN4JfAj4buAUyW0iP57Wgq52+5v/KuALZvYxNt8D0XX6bTM7BjTSkDgBPBv4L7t8vz3pNnlN1EyCU7tqtUEQxPzfNz6ezyzfxQce+kvWozpPf/hRvuNRT6BSjjYNwSxSaOo0eU0c25Yhp3nJmpAx33zjDVx3uMx77/sgS5fXeOI1R/jNRz+RyZopJIoMkFVK1E4e72XXBXd/Uq/v6+6Lue/fZWa/Y2bz7r7Q4+vteeptuWJ7rs95213IzY/4ufmJtStKY1lgChrZ4vIBtlQiXpiinq6TWF+rYGGyRNUNxy4wd2SRQ1efT4abXnOR4OhluGp1S0gsamjKT15TWowJlpJg3Dibrg2ZWxfy2PELzMwuM3N4kcPXnUuGmx5LJq+Jj9VbM5wqJIocrCuoz4Xh7i80s28kmfF0Hngv8IWdQiLsPij2MlzoGuCN6fCWAPhjd9/uavOedRp+2e0evSgKCYLkl2sYRpTTSWq+bu5qnvGw+U0zfuZDYlhp7uch74uO55x2E6PmRkgM02JiYZyec5MnXDXPk6+f27j/Up1EkYHzepPVr5w98Pc1s6uBh9zd06EoAXDuCl5vz1NvyxXbc30+yAu5ednwy2DNYS1MOmuLNRqXq9QvVwGYPbJIudxgam6F6aOLTF97IbknMTfcdJhC4pZgfKlGtFLZdM7HTpylvlpl+vASU0cXmb5+IbkncW6N6Np1dRJFBmhQ9Xm/ufv7gfeb2RzwIuBNZtYEXu/ur+u237ZB0czME12HDGXbdDigTwM37/oMrkD7kNMsMGXdxEql0do2664lQWnrEhhFnrimPRgDxOm46SwcA61ATHr8QRATlmJK5QZBKe4YiIt4viJjpQ+j58zszcAzSYaoniYJFWUAd38t8ALgx9JisQq8sNPv8z3Y89Tb0psrqc8cwIXcTceRG34ZrDrB5QBbLhMvV4kuV4nWS61aFgQxx244w+TRRarp7KabumpDEBIzW4Lxeol4uUpzpUpUT/78CoJk7eLZYxeZOLpE9fgSpWNL2OE13ZMoUhTFHd2+Z+5+CXgt8FozexzJclZd7dRRfL+Z/Rnw5+7+lexBM6sAXw+8GHg/yRTsB2qnbmK7LCzmu2ulcqM1icswhMTNP2/uJrZrdU9LMRYk59x+rgqJIsXgfVrQ191ftMPzryZZPmO/7HnqbelZz/X5IC/kWnpRM+usBWuGrYT4SoVotUJQa1CeXKc2vUYQrhBWmtQOr1BJl4Ow+fT+vCEKiV2D8YUJ4vUSpal1SperTB5KzjesNJg4tkhlfhmbXof5VZrHktlNFRJFBqtf9bkI0snrXrbdNjsFxecC/w/wZjN7GHARqAEhyRTov+Xun9yHY92T9llOgVY3MS8MI6K2n8uVJhbElKsbHcVhGX7ZrZuYsSAmLLX9HEYEpXjTuSokihSNDXUhupKpt6VnhazPnST36cVYw7FGAKslPL1NwoKYypEVAMJqk3BqnfKhy8lQ0+kG0ZFkEpe4PDwhEWgthWEREBkebfx9ElSaTBxbpDy5npzz5Hoy1HSqjh+uEx2KFBJFCmO46/OV2ikovg34Cen8iT4AACAASURBVHf/nXRR3nlg1d0v9v/QtudROuQy11nLtAJTMzcMEyhVmq3gNEz36HXrJuYFpeQ8I2jNdJoFxKAUtc4165oChT1fkXE05IWo56m3pWeFrc+Z/H161qQVmrZsF8TUrrlEOFHHptexw2v4VEQ0E7cFpuGoV1k30ZpgDcPWAryx+UJ2UGlSm1kjmF7Hpuswt048E6Wd00DrJIoUyJDX5yuyU1B8A/DXZvYG4P919wf6fkQ7yIacJt9vHXKZyXfX2jtrwzj8sr2b2ElQiluBsVNAzJ8rUOjzFRk3Q16Iep56W3r2BgpWnzvJumrWSIZg0jCYaGJRnbARwkSynaWByWvRUAemLBgHjeTeREsDok0ny1zm/+iyw6tYtYnPNJNzHqLOqcg4GfL6fEW2DYru/sdm9pfAy4HbzOz3gTj3/Cv7fHxdbVomom0IZtZRy7prw9xZa+8mdpLvmgIEpWwSG4VEkaEw5Av6XsnU29KbItdn2OgmQhKaALzs2Bp46K3gBMncAT7dwGsx8WTcmsAlLg9XYMqGnVrbf/UeOkacnHM5xsIYSnErFMc1b3VOh+2cRUbekNfnTDrL+Q8D30YyAqgEXAA+DPyhu7+r0367WR6jAawAVWCGXCE6aN1CUxA4lCJosumMugXEYQtN3mGoDuTOu/0x2HSuwFB0TUXGlTP8Vyx7nXpbrkhh6nM37aGJskPD8NBhbh3KTlyL8bIT16zVRRzWwJSFYsuvrFV2HDBiqK3jrfPNAmKQO1/VaJEiGYX6bGYvILmQ+8fA97n7l9PHp4AnAs83s59rn2cAdl4e47nAK4Fbga9194FPb75pptO2YZhBFhbp3FUDhmaoabv2kByUIuJm2AqG+cc7nWvy8/Ccr8g4CaplJm44NujD2Bd7nXpbelPE+pyxDrdIeNmIcQJiqCX37nnZk6+QDh214apX+Q4qgJcgrhkQJ/cphpb8wZkGRC8PfygWGQcjUp/f7e5/2v6gu68AHwQ+aGbTnXbcqaP474F/mU6fWihZGMrCYiswpd1DyIXFIewiwjYd1DAmjoLW+eUfz/6Znd8wna/IuIrXm1y+Z2HQh7HvdjP1tvSssPU5z0OgkQSnpKeW1uo0LHkpCYejGJiysGihQy0bfrtxzlEtnW9hhM5ZZNSMQn129+VOj5vZLPCvSNZXfjOwZbud7lH8hv04wP2wXWja9HPb46MSmoIwIo7CjZDYft65IaYw3OcqMo6GfWiLHKwi1eedxOXkv+0Ax0vWeiwLh8BQdhG78RBikkltvEQrGLaeSwMxKCCKDIMRrs9/BLwbuAy8HdhSV3Zzj2LhZKFp4+fxCk3t55s8NprnKjIuRrgQyZhKwpARpF3EmI2wBPnANDyzmm4nO19IQnEUGhZtnC+M3jmLjINRqc9m9hbgV9z9n9KHjgNvAVaBn+q0z1AGxUynwJQ8PlqhyULHI2udRxaS8+eVnWv74yIyBHx0CpFIXj48UW5/fPQ6anHFCeobHcRxOGeRkTZa9fnfA//RzB4AXgH8PPBnQAj8cqcdhi4otsIf3cPQKIamLCyCAqLIqBmRWdV6mnpbRo+H8aYJbbIOWvs2o2rczldklI1Cfc64+13Ai8zsG/7/9u48SJL8ru/++5tHHd3Vx8z0zM7szuyukHYRYgUSrAWE4jGXhCUhS8IcjyDAD1iPZTvgAR6OJyRsIIBwhGzAhjAYvEZ6OB7MfXgRMhiDCEIBSLsrBDpWgHa1x+zu7EzP0Wcdmfn7Pn9kZlVWdfXMdO9UV1bl96Wo6DqnM1W5/c1P/i7SlsTfB77sektZ7b96e8mMhqBiQBJfh26en/Rvs2zcPs/rvhpTbYLqwW9lkU29/QvABdKpt0+o6grw2cB/Ab5cRP54mttojtZ+wUh9N5eh6Xr7NY/7a0x1HK4+l6lG50TkpIh8O+nF3H8CXAP+UET+8X6fmakWxdGxicWwmL8+b0b3ufi8MWZ+lLGoHMChp94286uKAamK+2zMvJvx+lz0u8D/S9pQ+BOq+nYR+Q3ge0Xkbar6ltEPzFRQhGoGpCruszFVo3t7q82MFzL1tjHGGFNms1yfRywDv0bao/Y1AKraAX4kGz6yx8wFRWOMmTdeLWDh7rWDf/ADt35bbrEbTr1tjDHGlNWh6zOUsUZ/P2lN7gLfV3xBVS+M+4AFRWOMmTLXjdn59Gwv6AuHm3rbGGOMKat5qc8Aqvq7pN1Pb5oFRWOMmTIF3HyMgTjw1NvGGGNMWc1RfR5LRO5T1Y/t97oFRWOMKYF5GCx/mKm3jTHGmDKbh/p8HT8rIj+gqn8y7sWZWR7DGGPmWVWn3jbGGGPKbF6Wx9jHW4AfE5FvGPeiBUVjjJm6uSlCv0s6cU0+9fYvAW8EXiEiBxoXYYwxxkzf/KyjOI6qrgNfQ7psxh4WFI0xZspU56NFkcHU2/8NWIV06m1V/RHgX05zw4wxxpiDOmx9vpkaLSKvE5G/FZFPicg7rvO+rxERFZH7b+nOpf/2AvBu4IfGvW5jFI0xpgTUlTL4HdQPcMCpt40xxpgym0R9FhEf+GngtcB54CEReVBVPzHyviXg24EP3vKNSP028POq+gvjXrSgaIwxJVDSFsIDUdXfIV0v0RhjjJkLE6rPrwI+paqPQ395qTcDnxh5348A/x74nklsBPDjqvpH+71oQdEYA4D47qbfq4n1Wr+VvHpA6zNOHPyDj9z6bTkMEflqIAHeq6rxmNfvBP4vVf3eI984Y4wx5pAOXZ/hRjX6DuDpwuPzwBcU3yAirwTOqep7RWQiQfF6IREsKBpjOFhIzN9vYfHWcd2YrceuTHszXog/Ar4b+Lci8hjwd0AHOAW8ErgCvGt6m2eMMcYc3Ausz2si8nDh8QOq+kB2f1wzpeZ3RMQD/iPwzYf95aNE5F8DP6WqGyPPfy5wUlX/1+hnLCgaU3EHDYnFz1lYvEXKOznNTVHVTeAHReSHgPuB+4Am6fXUf6OqF6e5fcYYY8yhvLD6vK6q+01Acx44V3h8Fni28HiJtJb+qYgAnAYeFJE3qWoxfB7EO4CvFZE3qur5wvPPk05os2dbLSgaUzGHDYZmcpS5GaPogA9lN2OMMWamTbA+PwTcIyIvAp4B3gr01zLMWv3W8sci8qfA97yAkAjw96RjHv9YRP6Jqn48+10XRCQc9wELiuZAXOKPfd7zkyPeEnMY1wuJXrD/ay62lsNJm4egaIwxxsybSdRnVY1F5NuAPwR84D2q+nER+WHgYVV98Jb/UhBV/R0ReQ74XRF5p6r+poh8NtAb9wELiuaG8nCoyfj/UMRXXOJbWCy5cSHxeuFw9H0WFifLgqIxxhhTPpOqz6r6PuB9I8/9wD7v/ZJb8Suzf+svReS1wAMi8jOkS1p907gPWFA0+xoNiONaEz0/QROxsFhCN9t6eKPvLP/e5y0slqp1XEGtR7AxxhhTLvNVn78iv6OqTwBfISKhqkb7fcCCotmjGBDz+y6btCT/6fkOz3f9cJiHRVMO+4XEPCDmYehG4xU18fD8ZN9QNWtuZj+mdcHDWhSNMcaY8pmX+qyq62Oe2zckggVFUzAaEF3ipbd4/5PrYlg05XC9kDgaEPfrepq3HOYzm856WCxu+/W6UE+LIrg5KUTGGGPMvJiH+iwibwNeDvySqj4y8toq8I+Bf6qqrx39rAVFA6Qn0uMConOyJyh6QYLrBQS1dF1tz2bRLI39xiEWA2IxHI57vyZe/z2jXU1nsfvpfmNsRy9wFFvFj/rih1/3WXrx8YN/8BO3fluMMcYYkzp0fYbS1GhVfbeIfCnwDhH5QtLlMDrASdJJbH4F+Jpxn7WgWHHFk+i4F/YDYtwLcM4bGxSIs7CYBUgLiuUwGvqK3UyLLYj5fQn2D0L7rY84SyHxZsbY9sdfTrlFPOkmbH7q6lS3wRgzm2Sfv9dqtXmujPue5/k7LstxPS/1WVXfD7xfRDzgbtK1jp9V1evunAXFCiu2Isa9YCggRt0QdXv/Iw2I0qMmBi9rUTTTd6OQmAdECRKk2KLopffHfdf7hcWy2y8gujH7k1/kKMNY23kZA2GMOTr7nUznr81zkKiKKn7HZdvnearP2XrHj9/s+y0oVpRLfJJegEs84m6Ic2mLYh4Qo156aCTZSbbvJ3ieA0ICIrza/P1hmlXFkHijgJgHQwAJC99hND4szpqbmYgpl46v9YZaxPOQeOQtjDpfhcgYMzn5SbTXS/9meJHiwsHfD1ezieVm3eh3PGoev+PSHtcVr88WFCsoDYhp62F6SwNi3poY90KSwkm17zvy02afkZYr32WhRKfefa9qxrUiFsci+vV4KCDmwbAYFvtCrhsWyz6RzX4BsR8SC+NsvazL7WhInCaFmR8sb4yZrOKJtBcpoEhWdv1E0f6fOZnLIFEF48JS+nz6+rjveNZbFct+XFe9PltQrJC8q2k+FrG3W0+DYhQS9QK67TpJ4hP1wqHPNZodfCABQqy7aRnsFxLHtSJ69bgfDiVMEC/9Q6uu8IcvYhAWubnxiWXpmjputt70+cGMva64r7HfD4vjTOuCh9p5nTFmH5J4Q+FBkkGIyDkE9fPnLSzOmtHANO47JgIXCh7z8R3fzD47suO+sM9HHY6rXJ8tKFbEaFfTXqfWb0Xsder0eiFRdosLASDI/kNsNDv9+17g8IIkvfnOWhOP2I1ColePhgJiPxyGw9+RRP5wWLxJZQmIMLj4kd8fnbEX9rYmOid7wuL0j1+pdNcWY8x4eUBMT6I1/RmDFFqaXEPQIOuql4VFMztGw5LfcXu+Y/XBNeenRhQvfPgdxYv27vPQcR0O7/vRtqBWuz5bUKyAPCTmXU17u3XiKKTTrtNt14l6Ie3sp8u6Hoa1KBuTOOD7CeI5PM/hedrvdjr9k+zquF5IzLuaerUYCbPAmAXE4gQ2+A6S7NrcSFgsdj11sYcmXhrASjbb6fXW/CwGxPx4zo9ld53WxGmuowiTGQMhIu8B3ghcVNX7xrwuwE8CbwB2gW9W1Q/f8g0xxhxYMST6HcVrpy0uEgkSFf8mu/5Jdf+ZGW9pqorRCwF+W5FI8TrD37GGaZdM10zfm/iFuj1j3U5Hj2uJwevoTR3X02JB0cytcSGx267T69TZ2V7oB8ReFNLrDQ6HBdKwGJP2SPT9NCD6frKnNdEcjZsNiV49TlsR63EaEH0H4UghCR3SCQZhEdBiUSpRq+GoG4XEPCAOhduAPRc+xplmt1M3mf+Ufh74KeAX93n99cA92e0LgJ/JfhpjpmRcePA6irfrQSIQCZII6iuE2fPZSTUhWeuLzlyAqJpxFwL6AbHjDb5jSFuTcYASL5e3Pt9Ivs9+ZyQUd/zh4xqyQTCD4xrSCyBHP+PpxOrzTLCgOMeSbKKaOOtimgbEGt12nd2dNCS22w1223V6vUFflcB3+FnLYa0ZE9YixHcEtZigFuN5mv60lsQjs19I9Otx2uW0EeHV434rojQjaKSvUWhN1O6N/5PXOA9iXukmsdkvJObLu+QBMRm33VlY9DztX+goy2RMfsNn5SWrB//gDSa4VtU/E5G7r/OWNwO/qKoK/KWIrIrIGVV97uAbY4x5oUbDg7+dBcGOj2yH6UW8yINWD8GhANmJtQZYt9MZ4bf9/oWAYNP1w5JsBf06rYC0ev3gBKAjs4DOysWA4ePajT+uAXyHNOOhfc7lF0CO2qHrMxxgEYrysqA4h4qT1uQhsbPT7Hc13d1eYGenyW67ztZOgzjx6PZ8FpsRMBiX6AcJYS3C9xPCMOq3Jgb1qBQn11UxLiT6tWjPeESvHqWtiPUYacRpQMxaEvtXJdkbFtUJ6rz0lnc3jYe7oPbfO8WWxtGQOAiH0l8DNEn8fvfZcbO7Do9LHITEaUs6Cdf+fmMav/oO4OnC4/PZcxYUjTlikngEO/RbEf0tkK0A6fjodg23Xe8PFfASD1np9MPiaECclQBRNWMvBGz5SMfLvuNav0ZLPZ08UFq9rOvpwLRC02EUWxG9KNvnK/sc14s9BNKwmM0/MO0LIFOsz6VgQXHOjIbEXqdGr12nvZ0Gxe3tBXZ2FtjebrCx3WC36xMngytUeVj0g0FX07wlsd+iaF1Oj8xoSAzq0dhJa7x6hLfYS1sR6zGEDm2MOVFIHEI8CIuRj0Z+v9upxv6+YxOnHRLzopHP2puHxLgX9lsR417xT5pHUBvM0pt3Pc1bE4vKcMHjkGMg1kTk4cLjB1T1gQN8ftwvtf+4jTlig5AIwabD3/KQHR+92sBt10nataHhAeIpNGJodNFQ0VBw+c3GJ5bSaGAKrmYXArZD3Mbge84vcnqRjx84qMewmOAaigbpZEWz0po4evEjuCLITpAGxKvNPcc1pBdxpRmjDZfua+G4ntY+2xhFMxfGhcT2VrM/HnFnp8n21gLXthbYaYdstAM6SbrsRcODenYiXqvF1MKIZrNLI7uF9agfEvOT6jKcXM+zcS2J40Ki3+wNuprm3VQarn8FUjrXD3h5C1zempgre0jM1/2MuiHqPJLYw7k0MPp+guc51HkEYTSYqbc/CVM5upzm0nWaDvXRdVW9/wX86vPAucLjs8CzL+DfM8YcUH4ynY9T8y8NwkN0aYlkp07SDQgWevgL3eEPh5oGxUKAMOUzNjBdC9GNBtGVReKNJkl2ATdcaQ/3iAndnosBs3A9b89xfc1HrtZwGw3iy4vEW83+ce01osE+hw71tb/P0z6uX0B9ngsWFOfI9ULi5kaL7Z0mVzcXuLJZYzsWdhXqhYskga/Uw5iFZpdms0tYiwhqEUGY/aynP8tycj3PbtTdNJ+0xqvHSDNCFnvQjNHFJPvjOlik1sMNh8U4HeOi7RDXDdDIw/WCPa2JZXAzITG/P25conhZsC6MTQRK0+V0YGrTbz8IfJuI/CrpJDYbNj7RmKMxOrGH11H8SyF6tUGy2aB3cZn2+lK2tJVPrdumAWlYzCYsc410so9igJiFlqYq2TcwrS8SXVugc2mZaLdO0gv651bhShsJE2ikLWuz1ppY3Oe8qynrTZLNNBi3Ly0Tt2u4xCfc7dI4toO/2E3nWPBdvzUxaZbhuLblMcwcKE5cUwyJW5uttBVxs8XVjSZXdkI2YthC6YiypB4tgbqnLDYjmo0eYS2i0exQb3apNXp7upxaSJys/Sau2dvddG9ITJZcNth9MKW0JOnMYnSygJh4aDcYGZvop8+XaDmM64XEfDxi3AuIonQgfJJ4+L7r///nB67fslicqbfY7bQ0x7JObHmMXwG+hLSL6nngB8nmj1PVnwXeR7o0xqdIl8f4llu+EcaYPUZDor8F3qV62sL0/BLd9SV2Ly+zfWUJyCbi8hN0uY3f6qa9R5ZiXENJmh5JY7pd88x441qL2aiTXF6g+9wqu+vL7FxpEffSaT0XVnZIeiEh7XS8XquHa6R1PWnMRmviaEj0L4Xo+gLxlUW6F5doX15i+/Jyf/mq1nGoLbWpNXtIq4u2ItyCwzVLEownVJ9nxcSCooicI52S/TTgSMfO/OSkfl+VHSQkXowjPs7H2JEtzug9HOcMiz4sL8Q0G2l308XF9lCX09pCtz/LaWlOrOfUjWY3HRcStRVBw5EsOXot4aEPtjl/PuLeF9X43JfVh39B7EEnGBqbWJzApkwtibnrhcReL+Djl9d5YvsapxsrvHLtFDU/IazFg9bEEnc5LdIJ1H5V/fobvK7At97632yM2c/YkPhco9/VdPPxU2xfXmZnY5F6s9v/O+bXBuPR8wCRn0yDTWBTNmND4nqT5PIiO0+tsX1xha3Ly8S9gOZSG0jH5/m1iGC5ndb3rDUxaXpTH6d3M/J9DrazFvJn67jLC8SXF9l+ao2tSyvsbrQQzxHW0/W6/VqMX4/x8gsgWSt5mYLxJOrzrJhki2IMfLeqflhEloBHROSPVPUTE/ydlZMuEZAuE5BPXJN3Ny2GxEvbIY8lF/if8ssk3l0gizyR/DbX9C6+qvGPWGl1WG7t0lrapdHsUGt0qWUtivnJtV+YGMTceje7BEY+JjFvScxD4mWU7/2+57nn3pB77w35nQ9s8Qu/tcmPfusaja6XzjDWCdDYw3WD9NYLcN2w3+UU2NOaeNTjE4uzm14vJO50hB//2J+yQIuXNO/goa3zvPfCh3nnK17Nquf3i9Dwchhl63KaUkDLW/uNMbfQ2JC4vkj3wgqbT69x5Zk1Nq4ss7nRotXa5eTtl6it9KgttQmWOsMBouHZBDYlNBoSgwuDVrXtJ9a4dn6Na+urbF5dptsNOX3HRZaObRE2u4QrbbzlTtqytpSUp2XtBiTxCDcZaiF3F5aIri2w+fgpLp8/ydbVJbY2lghrESdvv8Tiyg71pV2ClTbeSgddikmWXKmCcdXr88SCYjbO5bns/paIPEo67boFxVvEJX7WmhjQ263Ta9fptEfGJGYtiZcS5Y/l13Hha6hznACPUF7OY+4PuFz7JHe1TrO42GZhcZfmYpt6szs0LtFC4mTtFxIl60o5tE5iNnFN3t0076Lxsz93ja/+ukW+9DVNJBHe8PoFfvWXt/hvD27zttccT7ub5rdCl9P9ZjmF6YbE/jqJhTGJxe6mDz75Me6pv4gvP/EKRJRXy0t5ePNv+fUn/5p/9fLPxfPStT/HhcSytSZCtbu2mKNlPX6mx2/7Y0Ni55ljbDy9xuXzazz52DmubbZYaHZZXtkmqMUsnNiifmoL/8QOeqxHspoQL5fnZNoMjGtJzEPi1mOnuPrMGpeeOcn5p05zbWuBe178DGEtpnVik+baFuHJLeRYB7eUkLTSlrV4sdwtxvk+e1EeEmu4ywv01ltsfPo2rjyzxtOfvp2rV5cBOHfuAmE9YvHEJvW1LYKTW/1gHC+Xryt1levzkYxRzBZ8fiXwwaP4fVUwGhK7WUhsbzfT2U2ziWs2dwOuxPAcl+lJSIMThAgB4PuC+PfxUf1r/rfFZRZau9Sb3XRs4kKXWrPXn7zGTM71QqIXOCRIEM+lt3qcLkhbz6aOziatkRg++tE23/GOU5D9QROFt7ypxXf/iyv8sy+sQScNia6bTmLjOiFJNyhNSBz83uGQ6JwMTVwTRenPD18+z3fc+dWIKIgioty/8mLe9eRDg3G1WUgsHsdlDIlB3Wf13kMs6GvTzpjDsR4/UyCJhxfp8Ml01pKYh8RP//1dPPP8MQDO3HaZleObtE5s0ji5SXB8BznWIZmhVqaqybsVSzLc3bQYEp9/6jb+5uOfAcDa6jZLy9u0jm3RPLFF7dRm2rK2mJAsDZaG0BLWrdyeiWuuhWlIfG5lKCQ+/uRpAO4+d5HlY5u0jm+xcPoawYmdtCt1FozLdlwfuj7DXNToiQdFEWkBvwV8p6pujnn97cDbAc4uL0x6c+ZCPsmHS7x+d7xep5aFxQbtdoPddo3NnZCNWNhC6QkIbhASPQg8R0JMo5awvLLNQmuXWqNXGJNok9dM2riQmD8/dobTeow0YrSRQGH6aABPBZJ0YTxJFBIhiRRf03GJ6Syng5B4vclrphESB8d1FhITb2idRHXp8hf5tokIiuuHRBFFUTzR8k9eMyLuJFz52+ou6GuOlvX4OXrFk+ng6qDFpXthhWtPnOLSU6d48rFz/MWjpwk9eMVLLrF2ep3V01do3X41bWVa6eBW45lpZaqa4tjTYNMNQuKlJbYeO8XlJ09x4enT/PXHXsRfbQR80akOZ++8wLHbrrB8+xUapzfwTuyix3rEx5WkKf2WtbLab+Ka3nMrbD2VdqN+8rGzfOhj59hJ4PPuvsptt1/i2JkrLN1+hdradhqMVyPiY/THJZYpGFe9Pk/0bFBEQtKQ+Muq+tvj3qOqD6jq/ap6/4mF+ri3mILiWom9dq3fmtht1+l26mlr4naDzZ06O5FHnP19Oa0nWHYeTp8n8CD0HZ4XAx/mDefO9mc4bS7t9ltj/GwCGzMZoyExl89wmk9eI6FDwiTtcho6CNxg7SwfNEhv/+C+On/w3h0kTkMiDn79V3Z4zT2raDskadeGQmLSDUodEuNekLYoxh5xFBJl4xNzX3TyHH985a/6IVFE+fNrj3L/7ccLa36Wd/KaUaoHvxnzQlmPn8nb2+ISpLObXlpi+9ljXLtwnPULazzz/DHuPN7l/s+8wIvvfYKTd15k5dw69SxAuJO9kZNpC4llMjT29JoPG3XcZoPd88fZeO4419ZXeeKJMwSB44tOdbjvsx/ntnPPs3p2ncU71/FP7MBah2Q1GQqJZf2eB2tDgtdOl8DQq+msvdvPnODac8dZv3CCJ54+xamVLv/gMy5z72d9mrXb11k5u07jjqt4azvoWpf4uPZbT5Nm+Wr1YerzvNToSc56KsC7gUdV9T9M6vdUTX4yHXfDdKbTKKTXyVsS6+y26+x0anQjj46DQGA164r4Zu/reJ/7JXrecXpuEV8e4y13nOUVZ5apNdoWEqdsdFxi3uXUq0eDLqe+S8MiIB0PD0c+I9i//KoV3vkTl3nkz3vc+9KQjzwUcaxX51+97rZ+SMxnOC1bSBx+nHU5jf3+uMSksJ3iOwLf8ZV338N//uSf87Pnf48XN2/nfO95qG/z/a/4nNkLiYCr8BgIMx0H6fFz7pytpnUYo10RvS0f3a4RX1mkc2mZ7cvLbG8ssrHZYmmxy+ryNne9+GlOnF1n5dzgZNqd7A11RSzjyXSV+W0/7VKcjT2VrQB3tZkugXF5iZ2NRa5eWaEbBaytbnPXi55h7fZ1Vs+u07o7D4ltktXZ6FZcPK6DTdcPxsnlRbrrS+xcabF1NZ2QabHRY3VlJz2u70hDYvPcFeRYux8Sy9x6WvX6PMm//K8Gvgn4qIh8JHvu+1T1fRP8nXMtn+E0ndhDiLppK0uvFxL1QtrtBr1eQBx7xMngoK4LrNYdC/Vlvm/xW1ivf5IkvMbnnPxSbj+OtSSWDYr0SQAAIABJREFUwLhxiXmXU/EUCdygNbFgEBahpQH/6V+c4ZOP93jqSfjyVy1w93JrKCS6bliqkDj43ePHJeZdTkeFYYQfOL7n817Fc73LPL1zjdesnuHetaWZC4m5ebn6aGbDzfb4AR4AeOXnN+wIPaD8ZNqLspPpLQ+5WkvX0Lu4xO7lpf6QkcXFXc7cfpGV45scv2Od5bwlMWtxSZbon0xbSCyXfkjsZGNPsxbj3nqL3fVldjcW6XXqRL2Qz7znaVor2xw/fZnVs5cLLYltkpMxrln+bsXF4zoNxl4/GPey9T/jXkinXccPEl784vMsH9vsX/yon7mGd2IXbmvvCYll3ecq1+dJznr6AdLhUuYW2DsuMZ3QI+9y2m7X6fUCulH6lQa+spL9rIeOxWbE0mKHhWaXu1dOsLDYtO6mJTTa5XQcHZlcSDre4Gc74KUn6nxmvYZ2A5J2WNrupjC+yymAi9NWxiTxEc+R95IPwwgAP3AEYYQXOO5utnjJycbQmMRZC4kglZ5VzRwt6/FzNPon0x3F6wiy46M7NaIri/S2mvTaNcJ6xOlzF+i26yyu7NA6scnSnevUzmzgrXRwZzpDIbGMLS5VJvmY+az7pWwF/RbjaLNJ1K4BsHLyGgBLxzZZWN1h+cwVFu5ex1vuzFRIzBWDseykreTRtQW6m016u+kwstvufJ7WxnZ6XB/f2nPxY1ZCYtXrs/UlmQF517y8y6mL/bQ1MUpbEpOsi16c+Pieo1GPCQJHHHsEgWOx0aPZ6NFa2qXZ7A4tgZFPXGMh8WgVxycWWxOBfpdTAAkTCAvfSewhHVDS5yRvOY48iL10ncTu3tlNyxoShx8PWhNheD3HoBb3WxbFc/h+MpjVtLBOYlBLg+RshcT0aqWz8z9zdKzHz4TtCRCd9GTa7dTSnh3ZGGxIa8DJuy7SWN2hcXKT2pmNdOKarLvpbJxMV89oy5rXEaTj47ZrJDu1dIhQN+y//+S5iyxmS2DUT28gx9r9MYmzEhJHu1JLx4d2gG7XSXbqJL2wX8MBTt55kYUTWzRObhKe3Eon61mNZigkWn22oDgjkl5QmAUyGFp8vBcFxIWTbj8LGXlArNcj6o0urdYujWaXWqNrIXGKRkNi8X7emghkS2IM/3XSxEtnNe0UnszGHQ5mNg3SrqbOK+0SGIPfP2hNzOVjE0flAdELHJ7n+iExqEczsU7ijVS5a4s5WtbjZ7KKs1/2A8RWkAaI7TriO/xaTGOp3V+6Z2Ftk9raFv5qG1nbJTkZ4RoyMyfTVZNfCBi0GA9aE912HQkcQT1Klxpb6OLXYupLu+magSd20pk++61q5Vs3cJx8n4eO646H7tSINxt49Qi/FlFr9mi0Ovi1ePi4nuGLH1WuzxYUS26oy2lhApsoCkmSwQl1LYxIYo8gO0H2PUe9HhHWIprNLvUsHDaaXWpZl9OgHvWL1CyeWM+icTOdjrYmAnu7nRY+p4kHebCK0vsae2lLYuSnQbEXoLFf2pA42poIDLUmep7b89dpNCCO62oKsxkSodqD5Y2ZF8UA4UVZgOj4aDeAKP275y90aa5BuNDFr8f4i13C1d20S14rIjk+ugTGbP5Nm3dDLWtR2pqoSTquXjxH7fgOkNZ9vx4THtvBz9YMnLVWtVzaguqQSJEoHe6i7bTV1KvFNE9uEtQj/HqMV4+orW0jx9pIq0dyMiJpZZMxzdA+Q7XrswXFGdAfv+UEF3sksTd0kp+fVDebXZzz8DyHHyTUwphGs0NYi/rjEevNrBXRQmLp7LdcBpGPAkI8eC77/jX20ted9LuaauyVOiQOtmHv2MSiYlj0slbyeQ2JQcPnxEtXDv7Bv7j122KMeWHyACExSCQQ7T3J9Be6BCtt/GYPaXWRY/ki625muiFW1ejFAEmyOQLi9KKteA5lEBb9Zg+v1U1bEQsXAmYpMBW72UoMXkegU1jXOHRo5OHVYuprW/itLt5iD1npoEsx8Z7utbNTqw9dn2EuarQFxRIrtibmywUU15HLT64D36URIoDQi6jXIzzP0Wh2CGoxjWaXIIyGupp6foJfi8f/YnMkRmc6BdJJbLxC66GTtPtpFhaHZAExb0XMu5pqks0WWtKQOK41scgLksHYnawFMb0/HBCBGZy0Zry4k7D+aHUX9DVmXuQn016k/VYXSQTqMTQj/KzXhHiaBsRGjLYi3FKSdcnzUB8LiSU39mJAM0aSHl7kp/MLAFKPkVYvbUUcuRDgwtkIibl+KB49rltdPCeQLYXuLfaQxR40Y9xqPPPHddXrswXFkipOYNNvTcy6mTrnIb4jzCbu8DxHPlza99MT6VototZIg2GYtR7aeMTp2m9s4o0Uw2L+WPv3vaFWxHzSGmBPSJx2QCzKWxOLvCCBrPtpMRz2X4O5akUsUqo9BsKYeSAjf2Ml+9OkviI4ZKWTXuQK0/Ho2orQhsMtuH5X01kLD1WUXwyANDSBQKiQCNLqpc9n75V6jC7FuIYbE5hmo3blrYmg/f2GwnHd6qXzkmfHNc0YXUz6x7ULZWZDIlh9tqBYYpqM7xPteY4k8dPQ5zt6vRA/+4OTPpcQ1mKCcBAO8wk/LCROx77dSkdo7KX/VUZAmP2EodbEfPZPjdKxEOO6mgKlDIk305pI7A8/hrGtiPn9eeFsbhFjZl6/1WWkw07/pPpYB2041FfcgssmrEnDg4XE8iteDCiGJhgsXSWtXnq/4XCh4ho6s62IRfmFj/wnoWbDYub/uK5yfbagWHJ5t9NRvp+ka8z5jnqzm7Ykeq6/vly/i2mQ9LuazkMXvVm0X0gsTmIzSp03CIv5c5E39HqxFbHY1RTKGRKvx/Ndvyt1MRwO/8y68szjcazVvmJpzKwbbU3UAFxDAEf+igIaanrzGRMe5uzv2pwpfsf91uJQSEckOsRPw4QCbsGhfvr6LAem4njMXP+43vXA132Pa0j3OWnO+HFd8fpsQbGkxnbN8xxhPW1iUucREpPEHn42ri1fOiCoRWOXDZi7k+s51w+LDFoRgZsOiDBbIdEbKZ7jAmLx8TxRYHZOHYwx4+Td83LFsAj0g4MGg+54sxgeTPpdOgSv36amSKj919LvmaELATAf3/PocQ3p4/HH9ezX66rXZwuKJTRu7JbrpV9VHhbzQBCE45cNsFbE8iqOT9TE67cqjgt1SjEg+v335d//LIXEPce1n/SfGw2J+esw3wGxSCvctcWYeePCdBybBpC0Bq0r6g9en5cT6arTAEBwzfTxaFia9YCYXwDph+MxxzUwV/s8qsr12YLijPA8hWw2SM9zeLX5Xi6gqvYLd/nzxYAIeyesudG/Uzb58ekSf8+xmh/DxffNsyp3bTFmHqQnyHkr0/C4ptGAOG8n0lXjwvR77n/H4fjvGGZzApei/nEdDcJirirHdZXrswXFEttzchz7eCNLWuQBMX2/hcRZlYc9L3BjQ97NBsTie8qm2IIovg5N1lQ8VqsWEHPzV1qNqZ78pBoYBMasxWWeT6SrJP0eIZ3tNB2/l3/Hxdfn6Xu+3nE9eH2+9rloPvfq5lhQLKniifT4bnnD4TD/TJVOrGeViz28wPVb0YrdT2/UOrhfN9Nx7y27YiAcVbXjOGj4rL10+eAffOjWb4sx5oXJT5pdLX9mvk+iq0J9NzShzfD3PP/fcVWP60PXZ5iLGm1BsYSGx24lWaBwQ4FxtAWmaifW86IYFq/3nv79fQIizEZILB7b416rqqiTcOnRzWlvhjHmkEZDxOhrZj7s9z3P63dsx7XVZwuKJeX5CQ4fTWTsCXRVu+fNoxutLQg3bmmcJXa8jlfhIRDGzIWqnDhXXdW+56rt7zhVrs8WFEssD4v7vWZmV3FM4o3eM86shkSzP1flSmSMMcaUVJXrswXFkrNAON+uFwb3YyFxPlW4DhljjDGlVeX6bEHRmAkrTlZz2M+b+Vb1BX2NMcaYMqp6fbagaMwROGxYtJBYHVW+YmmMMcaUVZXrswVFY47IQcOihcRqqfIVS2OMMaasqlyfLSgac4Qs/JlxFNAqX7I0xhhjSqjq9dmCojHGTFnY8Dn1WYdY0PeRW78txhhjjEkduj7DXNRoC4rGGDNlUSfhQoUX9DXGGGPKaJL1WUReB/wk4AM/p6rvGnn9u4D/E4iBS8A/U9UnJ7Ix+7B+cMYYUwJ6iJsxxhhjJusw9flGNVpEfOCngdcDLwO+XkReNvK2vwLuV9XPAX4T+Pe3Yn8OwoKiMcZMWT799kFvxhhjjJmcw9bnm6jRrwI+paqPq2oP+FXgzUO/W/X9qrqbPfxL4OwL3qEDsq6nxhhTAhb8jDHGmPJ5AfV5TUQeLjx+QFUfyO7fATxdeO088AXX+bfeBvyPw2/K4VhQNMaYErCupMYYY0z5vID6vK6q9+/zmtzsrxKRbwTuB7748JtyOBYUjTFmyvKuLcYYY4wpjwnW5/PAucLjs8Czo28SkdcA/xr4YlXtTmZT9mdB0RhjSkCtTdEYY4wpnQnV54eAe0TkRcAzwFuBbyi+QUReCfwX4HWqenESG3EjFhSNMaYELCYaY4wx5TOJ+qyqsYh8G/CHpMtjvEdVPy4iPww8rKoPAj8KtIDfEBGAp1T1TRPYnH1ZUDTGVJJL/H1f8/zkCLckXdD39GEW9J2DxXyNMcaYsjp0fYYb1mhVfR/wvpHnfqBw/zWH+8W3jgVFY0ylFAOiJoOx5OLr0HuOMixGnYRnp7eg7zeTXrV8Jnvqp1T15yayMcYYY8wMmWR9ngUWFI0xc2+/cFikiQyFxaN0M4vzHkZhQd/Xkg6cf0hEHlTVT4y89ddU9dsmsAnGGGPMzJpUfZ4VFhSNMXPNJf5QONyvy6nnJ9MNizKR39tf0BdARPIFfUeDojHGGGPGmFB9ngnetDfAGGMmwSV+PyTm9+NeiEu8Pbf8/TBocbzeGMaJbO8hbjdh3IK+d4x531eLyN+IyG+KyLkxrxtjjDGVdJj6PC9LXllQNMbMlWJATHpB9tgj7gVjA2L6mb1/Co9yjGK+TtMhitCaiDxcuL195J++mQV9fw+4W1U/B/hfwC/cgl0yxhhjXhBJvKHbNBy2Ps9LULSup8aYuVFsFcwDoks8XHz91kHPn/6f9EOu07Suqvdf5/UbLuirqpcLD/8r8O8OsyHGGGPMrbBfKMyf1yOu2VVe59iCojFm5t0oIDon/fteUGgpjP3hxzC1MYoTKns3s6DvGVV9Lnv4JuDRyWyKMcYYM14eAr3e+AnnXG16YW36l5Knx4KiMWamFSeriXth9lza1RTAxT7OFa5OxumP0YBYdNTrKKbXK299EbzJBX2/XUTeRPr/zBXgm2/5hhhjjDFjjAZEL9LsedChzkAypbA4mfo8KywoGmNm1vBkNYNWROdkEBpjjyTx8fPwF4DnOVzs49XiKW79QNjwuf2zVg7+wYdv/JabWND3ncA7D/7LjTHGmMMrhkQvUiQr03lYJAIXCurnz6VhURLvyLqfHro+w03V6LKzoGiMmUmjITHuhv0ups55RN00KGrWmjjURhhAUIv6Dz3f4fkJ4usUWhOh10k4/+jGkf9eY4wx5qjtDYiKFylee9Byp6GgQRoQHTLUuniUYxSrXp8tKBpjZk4xJMa9ABdnP7OAqM4jibNlL5xHWItR5yHe3uJSholsAHT8sAxjjDFmbkjiDQVEv61I1pro7XpomIZFTRTXSMPitFW5Ppfg/35jjLk547qaFoNiHIUksYdzaXfTvl7a3dQPAD/B8xyep3hBMvXWxP6+VXgMhDHGmPlXDIl+J21B9DqCRB4kgnQ88BXCNDyCwzUE0g5CU5vQpsr12YKiMWYm7NfVNO6F/VbEqBeQJD46OrV2FgDFc3iB609kk7cmTmum01y+TpMxxhgzj/y2329FDDZdGhA7PrIVQOyldbseI3ksG6nLLhRAp7A0RrXrswVFY0zp3Sgkxr2AKEp/5i2Jvp/gFwqKHzj8rDURGGpNhGnMdDqsyrOqGWOMmU95K6LfScch+tuKt+UjHQ/druG2axBlPYBaXaTVS8NiNpu5BqOznx69KtdnC4rGmFIbFxLjXtDvahr1AuJeSK8X4pJBl1Pne1CL8H2H57mh1sSgFg+1Jk47JEK1r1gaY4yZP6MhMbgKshUg2yG6U8Pt1Ei26/33+07wADnWAV9RP21JdGE62+lRtybmqlyfLSgaY0rrZkJit10n6oUkyfB6iflyGEEtGmpN9DzNWhJdKQIipF1bqnzF0hhjzHyRxCPYoT9hTXBFkGshutEgurJIslMj3qkTLHaHl6oKHeorbsGlM5/60xubCFafLSgaY0rpeiGx2673u5p22g2c8+h2QwLf4RfGH45rTSzTBDZFrsKzqhljjJkfeUjMJ6zxr/mw3sBdbRJdW6BzaZlot44mQrBbp3lyE68WI54ivkMbDvXTbqfTGptYVOX6bEHRGFM6BwmJ7XY9neU09nFBQg1oNDv4ftJvTQzrEUEt6nc5LVtIrDV8zh5mQd8P3fptMcYYYw5rT0i8FMB6E7fZoHdxmfb6EjuXl/tLWrWObxHUIxpnNpBWF21FuAWHawpJY7pdTuEF1GeYixo9saAoIu8B3ghcVNX7JvV7jDHz5WZCYt7d9LGrW/z+hQ9woXeFk+Ey/+j05/CyxeOEWSgMazFBGA11OQ1qUalCIqQL+j796LVpb4YxxhhzaKMhMbgQousLxFcW2X5ijd0rS2xdXiasR/3P+LWYYKGH3+whrR7aSJfESBrSb02cpqrX50m2KP488FPAL07wdxhj5ojLJ6LZb0xi1pIY9UI+dXWLdz/5p7x+4Q2cXbqDi+55fv2Z9/KNCy/nFSvHCcOIIEwDY1CLCepRfwKbstHsf8YYY8wsKoZEf1vxL6UhMXp+ie2n1rj6zBqbV5bZ3mjh+wm33fl82uunFuEvdJFjbbSR4BYcSdOb+gQ2uarXZ+/GbzkcVf0z4Mqk/n1jzHzJQ2LamlhcAiMNiZ12fai76e9f+AivX/hKzoZn8Xy4vXaSr1p6C//9/EfSkJgHxFpc2nGJRe4QN2OMMWbaxoVEd6FF77kVNj99G5efPMWzn76d80/czlNPn04nn4s9Fo9vs3DmGuFtW0irhzsek7QGE9hMOyTmDlOfy7HlL5yNUTTGlIYm6dqILvH66yQOlsBIWxKjXohzHs/3rnJ26fa0S6k4RJQT4QrbnV7a7bQe9SevSccmljckAvkSw8YcCRseYoy5FfaOScy6m15eZOvJk1w9f4ILT5/m45+8C4BzZy6zcnyD1oktFm+7Rri6i6x0cKtpSMy7nGqJanWV6/PEWhRvloi8XUQeFpGHL+92p705xpgp2DMuMVsbMeqGJLFHnC1/EUUhcZJOXHM8bLGuF9OQ6CniKdu6yUJNhiavmYWQqIe8GfMC/DzwumlvhDFmdg2WwKA/cY1ebRA9v8Tmp29j/clTPPvk7Xzor17MxY069TDm1Ol1Vk5eo3XqGrVTm2mX09WIZGmwZmLSLE+tPmx9npcaPfWgqKoPqOr9qnr/iYX6jT9gjJkrxZAY9wJc7Ge3NDBGUUgva0kECHxHWIt4w5mX8z9238uGXkM8x45u8Xvbv8fXvuyumQqJOSd64Jsxh2XDQ4wxL4QkHl5PkASCTdef3TS5vMjOM8fZuHCMK88f59G/O8diM+ZFt21x332PcerO51k9e5mFu9fxTuzCWof4uJIUZjktm8PU53mp0db11BgzNfm4xPS+lwbEwrhE5zw0Sa9nOZf+9IMEP0h4ees4QeNl/Pdnfp2ddkyzBl/3uXfxD+86OXMhEeZnPIOZHyLyduDtAOfO2emCMSaVh0S/o/htxd/ykO2QZLNB58IK2xdX2byyzOZmC4Bjy7vcc++TnLjjEqtnL7N45yAkJqsJSdMrxVIY+ynfFh2dSS6P8SvAlwBrInIe+EFVffekfp8xZjYVu5zm4xKTxCeJvfRn4uGysOh56Z/rsBZRq0V8XmuNLzj3xdSb3f6kNbMYEtNuKvNx9dHMD1V9AHgA4JWf37AD1BgDgNcTvEjxIsXfAtnxcRvZOomXl9jZWGR7o0US+3z2S5+ktbLN2u3rrJ67RPOOq/gndvohsSzrJe6n6vV5YkFRVb9+Uv+2MWb2jY5LLHY5Vef1WxN930EtXXPJ8x2+n+Bn6yHm6yTOckgEqDd87nzp6sE/OAeL+RpjjJkdknh4kSIJ+NuKbKXjEuPLi3SuLtLZWiCsR5y8/RKLrV2Wjm2ysLrDytl1mueuDLUkxsvlbkmEF1CfYS5qtPUlMcZMlctaDJ0TnEtbEUflYdH3HeK7/vIXvp8MLYExiyERoNuJeeKTV6e9GcYYY8y+BuMS0y6n3m7a5dRt10l26yS9dFkrIA2L5y6yeGKT5toW9dMbeGs76LEe8UlX+u6muarXZwuKxpgjd73WRIAk+ylZ8dDES5e8CCP8wCGe689s6nlKUI9Kv07ijZS3TJp5ZMNDjDEHIfkQkCgLiR1FdgJ0p0bSrqWvZRdva80eQS2isbpDfW2L4MQO3koHXevumbimzCExV/4tnBwLisaYI5WHxPR+PlGNDL3HDxzEg8deISDmrYiDFkQ38yFRqfY6Tebo2fAQY8xBpRPYOCRKWxNpB2g7RCMPrxHRXNtCfEdQjwgWeoTHdvBP7CCtHu5kj2SJmQuJVa/PFhSNMVORz3jq4uynG6zWI57DD8DPruPlAdELHJ7n+kHRy8YqAjMbEnNVHixvjDGmvPIup16kSJyum0gi/VnJIavTC10WGhF+s4fX6qatiK2I5HhM0srWSJyhkJircn22oGiMOTJuZPyhKxQZz3P9v0hJ4iPZDKfFgOgFydx0NR01OyXTGGNM1eQT2OStidLxwHfQjPCdoFFa371WF2n1kFYPXUxIltxgZtNQSJqzV6+rXJ8tKBpjjlRxbOKoPCx6gRt6Lg+IeSvivIVEFeZmcV5jjDHzI29NhHQ5jJz6ijRjBPAgDY2hQ+oxuhTjGi7rauqhPsSLoDNYr6teny0oGmOOxGhr4igvSHCx318rMX9uv4AIzEVIzFW3DBljjCk7SYZ/QiEsBg5tJBAqruFwDR1qRZy1rqajqlyfLSgaY6bKCxLIxikSpBXI87T/mpcVl3kOiVDtwfLGGGPKpzjTac41BHDkfYI0EVhM0FDRUHEN6bcizkNIhGrXZwuKxpgjk892mvN8h0u8Qmti2nqYv5b/LAbE9Ln5Com1hs+LDrGg7wc/OIGNMcYYY/ahwSAs9p/zQUNBg9FWxNmv1YetzzAfNdqCojFmKvohMfsZ1KOh19KfaZGZ11bEXLcT83iFF/Q1xhhTTvn4RPXBkc58modFzVKEC2WoBTF9/2y3IuaqXp8tKBpjjoz4iiaC5ye4xO8HQmDkfjUCYlGVu7YYY4wppzT4Sb/7qQuznkFh+mM0IKbPzUdIzFW5PltQNMYcqWJYBPqBsRgGqxQQc1UuRMYYY8qrHxZH6lQeGtPX5y8g5qpcny0oGmOOXB4WYW/rYfG5qlCqXYiMMcaUWx4WB/dhnrqY7qfq9dmCojHmSOTdTXPFYFh8T1VVuRAZY4wpH/Vdf+ZTKAbE+W09HKfK9dmCojHmyBS7m44+V2UKOLnh24wxxpgjVaVAOE7V67MFRWPMkbNwOEorfcXSGGOMKadq12cLisYYUwJVLkTGGGNMWVW5PltQNMaYKas1Aj7jpccO/LlP/uUENsYYY4wxwOHrM8xHjbagaIwxU9btxPz9J69MezOMMcYYU1D1+mxB0RhjSqDKXVuMMcaYsqpyfbagaIwxU1b1dZqMMcaYMqp6ffZu/BZjjDGTpSTiDny7GSLyOhH5WxH5lIi8Y8zrdRH5tez1D4rI3bd454wxxpgZdbj6fDM1ehbqc6laFIM7PoMT7/qNaW+GMcYcqUldsRQRH/hp4LXAeeAhEXlQVT9ReNvbgKuq+hIReSvw74D//ZZvjDHGGDNjql6frUXRGGNKIEEPfLsJrwI+paqPq2oP+FXgzSPveTPwC9n93wS+XEQqvLywMcYYM3CY+nwTNXom6nOpWhSNMaaK6o2Aez7rxIE/99yf3/AtdwBPFx6fB75gv/eoaiwiG8AJYP3AG2SMMcbMkcPWZ7hhjZ6J+lyqoPjII4+si8iTU/r1a1TrxMj2d75VbX/h6Pf5rlv1D23vPPGHf/bn37J2iI82ROThwuMHVPWBwuNxVx5HL3PezHtMxX3kw9311eanplWfwf6mVUHV9hdsnyelDPUZrl+jZ6I+lyooqurJaf1uEXlYVe+f1u8/ara/861q+wuzvc+q+roJ/dPngXOFx2eBZ/d5z3kRCYAVoLqLRpmxplmfYbb/+z6squ1z1fYXbJ9nQdXrs41RNMaY+fUQcI+IvEhEasBbgQdH3vMg8H9k978G+BNVtRZFY4wxZnJmoj6XqkXRGGPMrZONafg24A8BH3iPqn5cRH4YeFhVHwTeDfySiHyK9ErlW6e3xcYYY8z8m5X6bEFx4IEbv2Wu2P7Ot6rtL1Rzn29IVd8HvG/kuR8o3O8AX3vU22XMAVXxv++q7XPV9hdsnyttFuqzWA8jY4wxxhhjjDFFNkbRGGOMMcYYY8yQygdFEXmPiFwUkY9Ne1uOgoicE5H3i8ijIvJxEfmOaW/TJIlIQ0Q+JCJ/ne3vD017m46CiPgi8lci8t5pb8ukicgTIvJREfnIyDTUxpgZZzV6vms0WJ2uQp0Gq9WzqvJdT0XkHwLbwC+q6n3T3p5JE5EzwBlV/bCILAGPAG9R1U9MedMmQkQEWFTVbREJgQ8A36GqfznlTZsoEfku4H5gWVXfOO3tmSQReQK4X1WrthaVMXPPavR812iwOk0F6jRYrZ5VlW9RVNU/o0Jrhqnqc6r64ez+FvAocMd0t2pyNLWdPQyz21xfHRGRs8BXAj837W0xxpgXwmoOSowjAAAD/0lEQVT0fNdosDo97W0x5noqHxSrTETuBl4JfHC6WzJZWfeOjwAXgT9S1bneX+AngP8HcNPekCOiwP8UkUdE5O3T3hhjjLkVqlKjwep0RVitnkEWFCtKRFrAbwHfqaqb096eSVLVRFVfAZwFXiUic9t9SUTeCFxU1UemvS1H6NWq+nnA64FvzbqqGWPMzKpSjQar0xVhtXoGWVCsoGwMwG8Bv6yqvz3t7TkqqnoN+FPgdVPelEl6NfCmbCzArwJfJiL/33Q3abJU9dns50Xgd4BXTXeLjDHm8Kpao8Hq9DyzWj2bLChWTDZo/N3Ao6r6H6a9PZMmIidFZDW73wReA3xyuls1Oar6TlU9q6p3A28F/kRVv3HKmzUxIrKYTfiAiCwCXwFUYnZEY8z8qVqNBqvTzHmdBqvVs6zyQVFEfgX4C+AzReS8iLxt2ts0Ya8Gvon0CtZHstsbpr1RE3QGeL+I/A3wEOnYh0pMRV0RtwEfEJG/Bj4E/L6q/sGUt8kYc4tYjZ77Gg1Wp6vAavWMqvzyGMYYY4wxxhhjhlW+RdEYY4wxxhhjzDALisYYY4wxxhhjhlhQNMYYY4wxxhgzxIKiMcYYY4wxxpghFhSNMcYYY4wxxgyxoGiMMcYYY4wxZogFRTNzROSciHxaRI5nj49lj+8SkTMicqD1l0Tkx0TkyyaztcYYY0w1WH02Zr5YUDQzR1WfBn4GeFf21LuAB1T1SeC7gP96wH/yPwHvuHVbaIwxxlSP1Wdj5ouo6rS3wZgDE5EQeAR4D/DPgVeqak9EHgc+S1W7IvLNwFsAH7gP+HGgBnwT0AXeoKpXsn/vEeArVfXCke+MMcYYMyesPhszP6xF0cwkVY2A7wX+I/CdWRF6EXBVVbuFt94HfAPwKuDfAruq+krgL4B/Wnjfh4FXH8nGG2OMMXPK6rMx88OCopllrweeIy02AGeASyPveb+qbqnqJWAD+L3s+Y8CdxfedxG4fXKbaowxxlSG1Wdj5oAFRTOTROQVwGuBLwT+bxE5A7SBxshbi1cvXeGxA4LCa43s88YYY4w5JKvPxswPC4pm5oiIkA6W/05VfQr4UeDHgL9j+CrkQdwLfOyWbKAxxhhTQVafjZkvFhTNLPrnwFOq+kfZ4/8MvBS4H3hMRF5ykH8sG3j/EuDhW7qVxhhjTLVYfTZmjtisp2auiMhXAZ+vqv/mgJ/5PFX9/sltmTHGGFNdVp+NmT3Bjd9izOxQ1d8RkRMH/FhAOjW3McYYYybA6rMxs8daFI0xxhhjjDHGDLExisYYY4wxxhhjhlhQNMYYY4wxxhgzxIKiMcYYY4wxxpghFhSNMcYYY4wxxgyxoGiMMcYYY4wxZsj/D6wMMlM0kMNlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                        # color min and max and using the plasma color map\n",
    "\n",
    "xmin = xmn - xsiz/2; ymin = ymn - ysiz/2;\n",
    "xmax = xmin + xsiz * nx; ymax = ymin + ysiz * ny\n",
    "\n",
    "plt.subplot(121)\n",
    "locpix_st(kmap,xmin,xmax,ymin,ymax,xsiz,0.0,3.0,df,'x','y','vr','Kriging Estimate','X(m)','Y(m)','Porosity (%)',cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "pixelplt_st(vmap,xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Kriging Variance','X(m)','Y(m)','Porosity (%^2)',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.3, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Make a 2D Spatial Model to Sample Spaital Data\n",
    "\n",
    "The following are the parameters for the next demonstration.  This includes the number of cells in the 2D regular grid, the cell size (step) and the x and y min and max along with the color scheme.\n",
    "\n",
    "Then we make a single realization of a Gausian distributed feature over the specified 2D grid and then apply affine correction to ensure we have a reasonable mean and spread for our feature's distribution, assumed to be Porosity (e.g. no negative values) while retaining the Gaussian distribution.  Any transform could be applied at this point.  We are keeping this workflow simple. *This is our truth model that we will sample*.\n",
    "\n",
    "The parameters of *GSLIB_sgsim_2d_uncond* are (nreal,nx,ny,hsiz,seed,hrange1,hrange2,azi,output_file).  nreal is the number of realizations, nx and ny are the number of cells in x and y, hsiz is the cell siz, seed is the random number seed, hrange and hrange2 are the variogram ranges in major and minor directions respectively, azi is the azimuth of the primary direction of continuity (0 is aligned with Y axis) and output_file is a GEO_DAS file with the simulated realization.  The ouput is the 2D numpy array of the simulation along with the name of the property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2c518cab358>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx = 100; ny = 100; cell_size = 10                               # grid number of cells and cell size\n",
    "xmin = 0.0; ymin = 0.0;                                          # grid origin\n",
    "xmax = xmin + nx * cell_size; ymax = ymin + ny * cell_size       # calculate the extent of model\n",
    "seed = 74073                                                     # random number seed  for stochastic simulation    \n",
    "vario = make_variogram(0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=500,hmin1=500)\n",
    "mean = 10.0; stdev = 2.0                                         # Porosity mean and standard deviation\n",
    "#cmap = plt.cm.RdYlBu\n",
    "vmin = 0; vmax = 16; cmap = plt.cm.plasma                        # color min and max and using the plasma color map\n",
    "\n",
    "# calculate a stochastic realization with standard normal distribution\n",
    "sim = GSLIB_sgsim_2d_uncond(1,nx,ny,cell_size,seed,vario,\"Por\")\n",
    "sim = affine(sim,mean,stdev)                                     # correct the distribution to a target mean and standard deviation.\n",
    "sampling_ncell = 10                                              # sample every 10th node from the model\n",
    "#samples = regular_sample(sim,xmin,xmax,ymin,ymax,sampling_ncell,30,30,'Realization')\n",
    "#samples_cluster = samples.drop([80,79,78,73,72,71,70,65,64,63,61,57,56,54,53,47,45,42]) # this removes specific rows (samples)\n",
    "#samples_cluster = samples_cluster.reset_index(drop=True)         # we reset and remove the index (it is not sequential anymore)\n",
    "samples = random_sample(sim,xmin,xmax,ymin,ymax,cell_size,100,\"Por\")\n",
    "locpix(sim,xmin,xmax,ymin,ymax,cell_size,vmin,vmax,samples,'X','Y','Por','Porosity Realization and Regular Samples','X(m)','Y(m)','Porosity (%)',cmap,\"Por_Samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we assume a variogram and calculate a kriged map with the same grid as the simulation and conditional to the sampled values. Note: we are not concerned about the variogram of the kriging being consistent with the variogram of the data. We are exploring! This specific run took about 15 seconds on my desktop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Estimated   10000 blocks \n",
      "      average   9.988539993628553  variance  0.9301056196601394\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import timeit\n",
    "\n",
    "xsiz = cell_size; ysiz = cell_size; \n",
    "xmn = cell_size*0.5; ymn = cell_size*0.5; nxdis = 1; nydis = 1\n",
    "ndmin = 0; ndmax = 30; radius = 1000; ktype = 0; skmean = 10.0\n",
    "vario = make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=45,hmaj1=100,hmin1=100)\n",
    "tmin = -999; tmax = 999\n",
    "\n",
    "kmap, vmap = kb2d(samples,'X','Y','Por',tmin,tmax,nx,xmn,xsiz,ny,ymn,ysiz,nxdis,nydis,\n",
    "         ndmin,ndmax,radius,ktype,skmean,vario)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the resulting kriging estimates with the data plotted and the kriging variance map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "locpix_st(kmap,xmin,xmax,ymin,ymax,xsiz,vmin,vmax,samples,'X','Y','Por','Kriged Porosity Map','X(m)','Y(m)','Porosity (%)',cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "pixelplt_st(vmap,xmin,xmax,ymin,ymax,xsiz,0.0,1.1,'Kriging Variance','X(m)','Y(m)','Porosity (%^2)',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.3, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this code and demonstration useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
